From 9e8ad3a5f2fdfc598323055d1bb31fed79998ac0 Mon Sep 17 00:00:00 2001
From: dennisbader <dennisbader@users.noreply.github.com>
Date: Sun, 10 Dec 2023 17:51:02 +0000
Subject: [PATCH] Update gh-pages to output generated at acbd488

---
 README.html                                   |  16 +-
 _images/quickstart_00-quickstart_7_0.png      | Bin 18787 -> 34775 bytes
 .../transformers/base_data_transformer.html   |   2 +-
 .../fittable_data_transformer.html            |   2 +-
 .../invertible_data_transformer.html          |   2 +-
 _modules/darts/metrics/metrics.html           |   2 +-
 .../darts/models/forecasting/baselines.html   |   4 +
 .../models/forecasting/block_rnn_model.html   | 177 +++--
 .../darts/models/forecasting/dlinear.html     |   3 +-
 .../forecasting/exponential_smoothing.html    |   8 +-
 _modules/darts/models/forecasting/fft.html    |   8 +-
 .../models/forecasting/forecasting_model.html | 117 ++-
 .../models/forecasting/kalman_forecaster.html |   2 +
 _modules/darts/models/forecasting/nbeats.html |   3 +-
 _modules/darts/models/forecasting/nhits.html  |   3 +-
 .../darts/models/forecasting/nlinear.html     |   2 +-
 .../forecasting/pl_forecasting_module.html    |   2 +
 .../models/forecasting/regression_model.html  |   2 +
 .../darts/models/forecasting/rnn_model.html   | 226 +++---
 .../darts/models/forecasting/sf_auto_ces.html |   1 +
 .../models/forecasting/sf_auto_theta.html     |   1 +
 .../darts/models/forecasting/tbats_model.html |   8 +-
 .../darts/models/forecasting/tcn_model.html   |   3 +-
 .../darts/models/forecasting/tft_model.html   |  15 +-
 _modules/darts/models/forecasting/theta.html  |  12 +-
 .../darts/models/forecasting/tide_model.html  |  15 +-
 .../models/forecasting/transformer_model.html |   3 +-
 _modules/darts/timeseries.html                |  10 +-
 .../darts/utils/data/shifted_dataset.html     |   2 +-
 _sources/README.rst.txt                       |  16 +-
 ...multi-time-series-and-covariates.ipynb.txt |  21 +-
 .../20-RegressionModel-examples.ipynb.txt     |   2 +-
 .../darts.ad.aggregators.aggregators.rst.txt  |   1 -
 ...arts.ad.aggregators.and_aggregator.rst.txt |   1 -
 ...gators.ensemble_sklearn_aggregator.rst.txt |   1 -
 ...darts.ad.aggregators.or_aggregator.rst.txt |   1 -
 ...rts.ad.anomaly_model.anomaly_model.rst.txt |   1 -
 ...arts.ad.anomaly_model.filtering_am.rst.txt |   1 -
 ...ts.ad.anomaly_model.forecasting_am.rst.txt |   1 -
 .../darts.ad.detectors.detectors.rst.txt      |   1 -
 ...rts.ad.detectors.quantile_detector.rst.txt |   1 -
 ...ts.ad.detectors.threshold_detector.rst.txt |   1 -
 ...darts.ad.scorers.difference_scorer.rst.txt |   1 -
 .../darts.ad.scorers.kmeans_scorer.rst.txt    |   1 -
 ...darts.ad.scorers.nll_cauchy_scorer.rst.txt |   1 -
 ....ad.scorers.nll_exponential_scorer.rst.txt |   1 -
 .../darts.ad.scorers.nll_gamma_scorer.rst.txt |   1 -
 ...rts.ad.scorers.nll_gaussian_scorer.rst.txt |   1 -
 ...arts.ad.scorers.nll_laplace_scorer.rst.txt |   1 -
 ...arts.ad.scorers.nll_poisson_scorer.rst.txt |   1 -
 .../darts.ad.scorers.norm_scorer.rst.txt      |   1 -
 .../darts.ad.scorers.pyod_scorer.rst.txt      |   1 -
 .../darts.ad.scorers.scorers.rst.txt          |   1 -
 ...arts.ad.scorers.wasserstein_scorer.rst.txt |   1 -
 _sources/generated_api/darts.ad.utils.rst.txt |   1 -
 ...rts.dataprocessing.dtw.cost_matrix.rst.txt |   1 -
 .../darts.dataprocessing.dtw.dtw.rst.txt      |   1 -
 .../darts.dataprocessing.dtw.window.rst.txt   |   1 -
 ...taprocessing.encoders.encoder_base.rst.txt |   1 -
 ...s.dataprocessing.encoders.encoders.rst.txt |   1 -
 .../darts.dataprocessing.pipeline.rst.txt     |   1 -
 ...transformers.base_data_transformer.rst.txt |   1 -
 ...dataprocessing.transformers.boxcox.rst.txt |   1 -
 ...s.dataprocessing.transformers.diff.rst.txt |   1 -
 ...sformers.fittable_data_transformer.rst.txt |   1 -
 ...ormers.invertible_data_transformer.rst.txt |   1 -
 ...ataprocessing.transformers.mappers.rst.txt |   1 -
 ....dataprocessing.transformers.midas.rst.txt |   1 -
 ...transformers.missing_values_filler.rst.txt |   1 -
 ...essing.transformers.reconciliation.rst.txt |   1 -
 ...dataprocessing.transformers.scaler.rst.txt |   1 -
 ...mers.static_covariates_transformer.rst.txt |   1 -
 ...ng.transformers.window_transformer.rst.txt |   1 -
 .../darts.datasets.dataset_loaders.rst.txt    |   1 -
 ...arts.explainability.explainability.rst.txt |   1 -
 ...plainability.explainability_result.rst.txt |   1 -
 ...arts.explainability.shap_explainer.rst.txt |   1 -
 ...darts.explainability.tft_explainer.rst.txt |   1 -
 .../darts.explainability.utils.rst.txt        |   1 -
 .../darts.metrics.metrics.rst.txt             |   1 -
 ...rts.models.components.feed_forward.rst.txt |   1 -
 ...rts.models.components.glu_variants.rst.txt |   1 -
 ...els.components.layer_norm_variants.rst.txt |   1 -
 ...els.components.statsforecast_utils.rst.txt |   1 -
 ...arts.models.components.transformer.rst.txt |   1 -
 ...s.models.filtering.filtering_model.rst.txt |   1 -
 ....filtering.gaussian_process_filter.rst.txt |   1 -
 ...rts.models.filtering.kalman_filter.rst.txt |   1 -
 ...ls.filtering.moving_average_filter.rst.txt |   1 -
 .../darts.models.forecasting.arima.rst.txt    |   1 -
 ...arts.models.forecasting.auto_arima.rst.txt |   1 -
 ...darts.models.forecasting.baselines.rst.txt |   1 -
 ...models.forecasting.block_rnn_model.rst.txt |  10 +-
 ....models.forecasting.catboost_model.rst.txt |   1 -
 .../darts.models.forecasting.croston.rst.txt  |   1 -
 .../darts.models.forecasting.dlinear.rst.txt  |   1 -
 ....models.forecasting.ensemble_model.rst.txt |   1 -
 ....forecasting.exponential_smoothing.rst.txt |   1 -
 .../darts.models.forecasting.fft.rst.txt      |   1 -
 ...dels.forecasting.forecasting_model.rst.txt |   1 -
 ...dels.forecasting.kalman_forecaster.rst.txt |   1 -
 .../darts.models.forecasting.lgbm.rst.txt     |   1 -
 ...orecasting.linear_regression_model.rst.txt |   1 -
 .../darts.models.forecasting.nbeats.rst.txt   |   1 -
 .../darts.models.forecasting.nhits.rst.txt    |   1 -
 .../darts.models.forecasting.nlinear.rst.txt  |   1 -
 ....forecasting.pl_forecasting_module.rst.txt |   1 -
 ...s.models.forecasting.prophet_model.rst.txt |   1 -
 ...s.models.forecasting.random_forest.rst.txt |   1 -
 ...ecasting.regression_ensemble_model.rst.txt |   1 -
 ...odels.forecasting.regression_model.rst.txt |   1 -
 ...darts.models.forecasting.rnn_model.rst.txt |  10 +-
 ...s.models.forecasting.sf_auto_arima.rst.txt |   1 -
 ...rts.models.forecasting.sf_auto_ces.rst.txt |   1 -
 ...rts.models.forecasting.sf_auto_ets.rst.txt |   1 -
 ...s.models.forecasting.sf_auto_theta.rst.txt |   1 -
 ...rts.models.forecasting.tbats_model.rst.txt |   1 -
 ...darts.models.forecasting.tcn_model.rst.txt |   1 -
 ...darts.models.forecasting.tft_model.rst.txt |   1 -
 ...s.models.forecasting.tft_submodels.rst.txt |   1 -
 .../darts.models.forecasting.theta.rst.txt    |   1 -
 ...arts.models.forecasting.tide_model.rst.txt |   1 -
 ...orecasting.torch_forecasting_model.rst.txt |   1 -
 ...dels.forecasting.transformer_model.rst.txt |   1 -
 .../darts.models.forecasting.varima.rst.txt   |   1 -
 .../darts.models.forecasting.xgboost.rst.txt  |   1 -
 .../generated_api/darts.models.utils.rst.txt  |   1 -
 .../generated_api/darts.timeseries.rst.txt    |   1 -
 .../darts.utils.callbacks.rst.txt             |   1 -
 ...s.utils.data.horizon_based_dataset.rst.txt |   1 -
 ...darts.utils.data.inference_dataset.rst.txt |   1 -
 ...arts.utils.data.sequential_dataset.rst.txt |   1 -
 .../darts.utils.data.shifted_dataset.rst.txt  |   1 -
 .../darts.utils.data.tabularization.rst.txt   |   1 -
 .../darts.utils.data.training_dataset.rst.txt |   1 -
 .../darts.utils.data.utils.rst.txt            |   1 -
 ...ed_historical_forecasts_regression.rst.txt |   1 -
 ...timized_historical_forecasts_torch.rst.txt |   1 -
 ...s.utils.historical_forecasts.utils.rst.txt |   1 -
 .../darts.utils.likelihood_models.rst.txt     |   1 -
 .../generated_api/darts.utils.losses.rst.txt  |   1 -
 .../darts.utils.missing_values.rst.txt        |   1 -
 .../darts.utils.model_selection.rst.txt       |   1 -
 .../darts.utils.multioutput.rst.txt           |   1 -
 .../darts.utils.statistics.rst.txt            |   1 -
 .../darts.utils.timeseries_generation.rst.txt |   1 -
 .../generated_api/darts.utils.torch.rst.txt   |   1 -
 .../generated_api/darts.utils.utils.rst.txt   |   1 -
 _sources/quickstart/00-quickstart.ipynb.txt   |   6 +-
 .../01-multi-time-series-and-covariates.html  |  19 +-
 .../01-multi-time-series-and-covariates.ipynb |  21 +-
 examples/14-transfer-learning.ipynb           |   2 +-
 examples/20-RegressionModel-examples.html     |   2 +-
 examples/20-RegressionModel-examples.ipynb    |   2 +-
 ...ng.transformers.base_data_transformer.html |   2 +-
 ...ransformers.fittable_data_transformer.html |   2 +-
 ...nsformers.invertible_data_transformer.html |   2 +-
 ...rocessing.transformers.reconciliation.html |   4 +-
 .../darts.models.forecasting.arima.html       |  24 +-
 .../darts.models.forecasting.auto_arima.html  |  26 +-
 .../darts.models.forecasting.baselines.html   | 124 +++-
 ...ts.models.forecasting.block_rnn_model.html | 675 +++++++++++++++--
 ...rts.models.forecasting.catboost_model.html |  21 +-
 .../darts.models.forecasting.croston.html     |  26 +-
 .../darts.models.forecasting.dlinear.html     |  38 +-
 ...rts.models.forecasting.ensemble_model.html |  20 +-
 ...els.forecasting.exponential_smoothing.html |  30 +-
 .../darts.models.forecasting.fft.html         |  26 +-
 ....models.forecasting.kalman_forecaster.html |  24 +-
 .../darts.models.forecasting.lgbm.html        |  21 +-
 ...s.forecasting.linear_regression_model.html |  21 +-
 .../darts.models.forecasting.nbeats.html      |  38 +-
 .../darts.models.forecasting.nhits.html       |  38 +-
 .../darts.models.forecasting.nlinear.html     |  25 +-
 ...arts.models.forecasting.prophet_model.html |  26 +-
 ...arts.models.forecasting.random_forest.html |  21 +-
 ...forecasting.regression_ensemble_model.html |  20 +-
 ...s.models.forecasting.regression_model.html |  42 +-
 .../darts.models.forecasting.rnn_model.html   | 678 ++++++++++++++++--
 ...arts.models.forecasting.sf_auto_arima.html |  26 +-
 .../darts.models.forecasting.sf_auto_ces.html |  26 +-
 .../darts.models.forecasting.sf_auto_ets.html |  26 +-
 ...arts.models.forecasting.sf_auto_theta.html |  26 +-
 .../darts.models.forecasting.tbats_model.html |  60 +-
 .../darts.models.forecasting.tcn_model.html   |  38 +-
 .../darts.models.forecasting.tft_model.html   |  68 +-
 .../darts.models.forecasting.theta.html       |  52 +-
 .../darts.models.forecasting.tide_model.html  |  68 +-
 ....models.forecasting.transformer_model.html |  38 +-
 .../darts.models.forecasting.varima.html      |  24 +-
 .../darts.models.forecasting.xgboost.html     |  21 +-
 .../darts.utils.data.shifted_dataset.html     |   2 +-
 genindex.html                                 | 164 +++--
 index.html                                    |  16 +-
 objects.inv                                   | Bin 97923 -> 98611 bytes
 py-modindex.html                              |  10 -
 quickstart/00-quickstart.html                 |   2 +-
 quickstart/00-quickstart.ipynb                |   6 +-
 searchindex.js                                |   2 +-
 199 files changed, 2640 insertions(+), 885 deletions(-)

diff --git a/README.html b/README.html
index be2257aa1a..f6ec1ef004 100644
--- a/README.html
+++ b/README.html
@@ -245,7 +245,7 @@ <h1>Time Series Made Easy in Python<a class="headerlink" href="#time-series-made
 <a class="reference external image-reference" href="https://badge.fury.io/py/darts"><img alt="PyPI version" src="https://badge.fury.io/py/u8darts.svg" /></a>
 <a class="reference external image-reference" href="https://anaconda.org/conda-forge/u8darts-all"><img alt="Conda Version" src="https://img.shields.io/conda/vn/conda-forge/u8darts-all.svg" /></a>
 <a class="reference external image-reference" href="https://img.shields.io/badge/python-3.8+-blue.svg"><img alt="Supported versions" src="https://img.shields.io/badge/python-3.8+-blue.svg" /></a>
-<a class="reference external image-reference" href="https://img.shields.io/docker/v/unit8/darts?label=docker&amp;sort=date"><img alt="Docker Image Version (latest by date)" src="https://img.shields.io/docker/v/unit8/darts?label=docker&amp;sort=date" /></a>
+<a class="reference external image-reference" href="https://hub.docker.com/r/unit8/darts"><img alt="Docker Image Version (latest by date)" src="https://img.shields.io/docker/v/unit8/darts?label=docker&amp;sort=date" /></a>
 <a class="reference external image-reference" href="https://img.shields.io/github/release-date/unit8co/darts"><img alt="GitHub Release Date" src="https://img.shields.io/github/release-date/unit8co/darts" /></a>
 <a class="reference external image-reference" href="https://img.shields.io/github/actions/workflow/status/unit8co/darts/release.yml?branch=master"><img alt="GitHub Workflow Status" src="https://img.shields.io/github/actions/workflow/status/unit8co/darts/release.yml?branch=master" /></a>
 <a class="reference external image-reference" href="https://pepy.tech/project/darts"><img alt="Downloads" src="https://pepy.tech/badge/darts" /></a>
@@ -450,28 +450,28 @@ <h2>Forecasting Models<a class="headerlink" href="#forecasting-models" title="Pe
 </tr>
 <tr class="row-odd"><td><p><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMean">NaiveMean</a></p></td>
 <td></td>
-<td><p>🟩 🟥</p></td>
+<td><p>🟩 🟩</p></td>
 <td><p>🟥 🟥 🟥</p></td>
 <td><p>🟥 🟥</p></td>
 <td><p>🟥</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveSeasonal">NaiveSeasonal</a></p></td>
 <td></td>
-<td><p>🟩 🟥</p></td>
+<td><p>🟩 🟩</p></td>
 <td><p>🟥 🟥 🟥</p></td>
 <td><p>🟥 🟥</p></td>
 <td><p>🟥</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveDrift">NaiveDrift</a></p></td>
 <td></td>
-<td><p>🟩 🟥</p></td>
+<td><p>🟩 🟩</p></td>
 <td><p>🟥 🟥 🟥</p></td>
 <td><p>🟥 🟥</p></td>
 <td><p>🟥</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMovingAverage">NaiveMovingAverage</a></p></td>
 <td></td>
-<td><p>🟩 🟥</p></td>
+<td><p>🟩 🟩</p></td>
 <td><p>🟥 🟥 🟥</p></td>
 <td><p>🟥 🟥</p></td>
 <td><p>🟥</p></td>
@@ -494,7 +494,7 @@ <h2>Forecasting Models<a class="headerlink" href="#forecasting-models" title="Pe
 <td></td>
 <td><p>🟥 🟩</p></td>
 <td><p>🟥 🟩 🟥</p></td>
-<td><p>🟥 🟥</p></td>
+<td><p>🟩 🟥</p></td>
 <td><p>🟥</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.auto_arima.html#darts.models.forecasting.auto_arima.AutoARIMA">AutoARIMA</a></p></td>
@@ -553,7 +553,7 @@ <h2>Forecasting Models<a class="headerlink" href="#forecasting-models" title="Pe
 <td><p>🟩 🟥</p></td>
 <td><p>🟥</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.prophet_model.html#darts.models.forecasting.prophet_model.Prophet">Prophet</a> (see <a class="reference external" href="https://github.com/unit8co/darts/blob/master/INSTALL.md#enabling-support-for-facebook-prophet">install notes</a>)</p></td>
+<tr class="row-even"><td><p><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.prophet_model.html#darts.models.forecasting.prophet_model.Prophet">Prophet</a></p></td>
 <td><p><a class="reference external" href="https://github.com/facebook/prophet">Prophet repo</a></p></td>
 <td><p>🟩 🟥</p></td>
 <td><p>🟥 🟩 🟥</p></td>
@@ -609,7 +609,7 @@ <h2>Forecasting Models<a class="headerlink" href="#forecasting-models" title="Pe
 <td><p>🟥 🟥</p></td>
 <td><p>🟩</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.lgbm.html#darts.models.forecasting.lgbm.LightGBMModel">LightGBMModel</a>,</p></td>
+<tr class="row-even"><td><p><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.lgbm.html#darts.models.forecasting.lgbm.LightGBMModel">LightGBMModel</a></p></td>
 <td></td>
 <td><p>🟩 🟩</p></td>
 <td><p>🟩 🟩 🟩</p></td>
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diff --git a/_modules/darts/dataprocessing/transformers/base_data_transformer.html b/_modules/darts/dataprocessing/transformers/base_data_transformer.html
index 9d46d121ca..ff217a8774 100644
--- a/_modules/darts/dataprocessing/transformers/base_data_transformer.html
+++ b/_modules/darts/dataprocessing/transformers/base_data_transformer.html
@@ -244,7 +244,7 @@ <h1>Source code for darts.dataprocessing.transformers.base_data_transformer</h1>
 <span class="sd">            each parallel job. If `parallel_params` is a `Sequence` of fixed attribute names, only those</span>
 <span class="sd">            attribute names specified will take on different values between different parallel jobs.</span>
 <span class="sd">        mask_components</span>
-<span class="sd">            Optionally, whether or not to automatically apply any provided `component_mask`s to the</span>
+<span class="sd">            Optionally, whether to automatically apply any provided `component_mask`s to the</span>
 <span class="sd">            `TimeSeries` inputs passed to `transform`, `fit`, `inverse_transform`, or `fit_transform`.</span>
 <span class="sd">            If `True`, any specified `component_mask` will be applied to each input timeseries</span>
 <span class="sd">            before passing them to the called method; the masked components will also be automatically</span>
diff --git a/_modules/darts/dataprocessing/transformers/fittable_data_transformer.html b/_modules/darts/dataprocessing/transformers/fittable_data_transformer.html
index 15796e6d34..03f3309667 100644
--- a/_modules/darts/dataprocessing/transformers/fittable_data_transformer.html
+++ b/_modules/darts/dataprocessing/transformers/fittable_data_transformer.html
@@ -223,7 +223,7 @@ <h1>Source code for darts.dataprocessing.transformers.fittable_data_transformer<
 <span class="sd">            each parallel job. If `parallel_params` is a `Sequence` of fixed attribute names, only those</span>
 <span class="sd">            attribute names specified will take on different values between different parallel jobs.</span>
 <span class="sd">        mask_components</span>
-<span class="sd">            Optionally, whether or not to automatically apply any provided `component_mask`s to the</span>
+<span class="sd">            Optionally, whether to automatically apply any provided `component_mask`s to the</span>
 <span class="sd">            `TimeSeries` inputs passed to `transform`, `fit`, `inverse_transform`, or `fit_transform`.</span>
 <span class="sd">            If `True`, any specified `component_mask` will be applied to each input timeseries</span>
 <span class="sd">            before passing them to the called method; the masked components will also be automatically</span>
diff --git a/_modules/darts/dataprocessing/transformers/invertible_data_transformer.html b/_modules/darts/dataprocessing/transformers/invertible_data_transformer.html
index 910aa34c70..c0793b5431 100644
--- a/_modules/darts/dataprocessing/transformers/invertible_data_transformer.html
+++ b/_modules/darts/dataprocessing/transformers/invertible_data_transformer.html
@@ -232,7 +232,7 @@ <h1>Source code for darts.dataprocessing.transformers.invertible_data_transforme
 <span class="sd">            each parallel job. If `parallel_params` is a `Sequence` of fixed attribute names, only those</span>
 <span class="sd">            attribute names specified will take on different values between different parallel jobs.</span>
 <span class="sd">        mask_components</span>
-<span class="sd">            Optionally, whether or not to automatically apply any provided `component_mask`s to the</span>
+<span class="sd">            Optionally, whether to automatically apply any provided `component_mask`s to the</span>
 <span class="sd">            `TimeSeries` inputs passed to `transform`, `fit`, `inverse_transform`, or `fit_transform`.</span>
 <span class="sd">            If `True`, any specified `component_mask` will be applied to each input timeseries</span>
 <span class="sd">            before passing them to the called method; the masked components will also be automatically</span>
diff --git a/_modules/darts/metrics/metrics.html b/_modules/darts/metrics/metrics.html
index 2cd66a6fe3..49fc7b63ae 100644
--- a/_modules/darts/metrics/metrics.html
+++ b/_modules/darts/metrics/metrics.html
@@ -351,7 +351,7 @@ <h1>Source code for darts.metrics.metrics</h1><div class="highlight"><pre>
 <span class="sd">    series_b</span>
 <span class="sd">        A univariate (deterministic or stochastic) ``TimeSeries`` instance (the predicted series).</span>
 <span class="sd">    intersect</span>
-<span class="sd">        A boolean for whether or not to only consider the time intersection between `series_a` and `series_b`</span>
+<span class="sd">        A boolean for whether to only consider the time intersection between `series_a` and `series_b`</span>
 <span class="sd">    stochastic_quantile</span>
 <span class="sd">        Optionally, for stochastic predicted series, return either all sample values with (`stochastic_quantile=None`)</span>
 <span class="sd">        or any deterministic quantile sample values by setting `stochastic_quantile=quantile` {&gt;=0,&lt;=1}.</span>
diff --git a/_modules/darts/models/forecasting/baselines.html b/_modules/darts/models/forecasting/baselines.html
index 7ef64eeb50..d22d5bb04a 100644
--- a/_modules/darts/models/forecasting/baselines.html
+++ b/_modules/darts/models/forecasting/baselines.html
@@ -228,6 +228,7 @@ <h1>Source code for darts.models.forecasting.baselines</h1><div class="highlight
         <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
         <span class="n">num_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
         <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">show_warnings</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
     <span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">)</span>
         <span class="n">forecast</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mean_val</span><span class="p">,</span> <span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
@@ -292,6 +293,7 @@ <h1>Source code for darts.models.forecasting.baselines</h1><div class="highlight
         <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
         <span class="n">num_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
         <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">show_warnings</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
     <span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">)</span>
         <span class="n">forecast</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">last_k_vals</span><span class="p">[</span><span class="n">i</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">K</span><span class="p">,</span> <span class="p">:]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)])</span>
@@ -341,6 +343,7 @@ <h1>Source code for darts.models.forecasting.baselines</h1><div class="highlight
         <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
         <span class="n">num_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
         <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">show_warnings</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
     <span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">)</span>
         <span class="n">first</span><span class="p">,</span> <span class="n">last</span> <span class="o">=</span> <span class="p">(</span>
@@ -411,6 +414,7 @@ <h1>Source code for darts.models.forecasting.baselines</h1><div class="highlight
         <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
         <span class="n">num_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
         <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">show_warnings</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
     <span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">)</span>
 
diff --git a/_modules/darts/models/forecasting/block_rnn_model.html b/_modules/darts/models/forecasting/block_rnn_model.html
index 48214afaad..47ca6d5a6d 100644
--- a/_modules/darts/models/forecasting/block_rnn_model.html
+++ b/_modules/darts/models/forecasting/block_rnn_model.html
@@ -170,12 +170,14 @@ <h1>Source code for darts.models.forecasting.block_rnn_model</h1><div class="hig
 <span class="sd">-------------------------------</span>
 <span class="sd">&quot;&quot;&quot;</span>
 
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span>
+<span class="kn">import</span> <span class="nn">inspect</span>
+<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABC</span><span class="p">,</span> <span class="n">abstractmethod</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Type</span><span class="p">,</span> <span class="n">Union</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
 
-<span class="kn">from</span> <span class="nn">darts.logging</span> <span class="kn">import</span> <span class="n">get_logger</span><span class="p">,</span> <span class="n">raise_if_not</span>
+<span class="kn">from</span> <span class="nn">darts.logging</span> <span class="kn">import</span> <span class="n">get_logger</span><span class="p">,</span> <span class="n">raise_log</span>
 <span class="kn">from</span> <span class="nn">darts.models.forecasting.pl_forecasting_module</span> <span class="kn">import</span> <span class="p">(</span>
     <span class="n">PLPastCovariatesModule</span><span class="p">,</span>
     <span class="n">io_processor</span><span class="p">,</span>
@@ -185,11 +187,9 @@ <h1>Source code for darts.models.forecasting.block_rnn_model</h1><div class="hig
 <span class="n">logger</span> <span class="o">=</span> <span class="n">get_logger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
 
 
-<span class="c1"># TODO add batch norm</span>
-<span class="k">class</span> <span class="nc">_BlockRNNModule</span><span class="p">(</span><span class="n">PLPastCovariatesModule</span><span class="p">):</span>
+<div class="viewcode-block" id="CustomBlockRNNModule"><a class="viewcode-back" href="../../../../generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.CustomBlockRNNModule">[docs]</a><span class="k">class</span> <span class="nc">CustomBlockRNNModule</span><span class="p">(</span><span class="n">PLPastCovariatesModule</span><span class="p">,</span> <span class="n">ABC</span><span class="p">):</span>
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
-        <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
         <span class="n">input_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
         <span class="n">hidden_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
         <span class="n">num_layers</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -199,24 +199,22 @@ <h1>Source code for darts.models.forecasting.block_rnn_model</h1><div class="hig
         <span class="n">dropout</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span>
         <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
     <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;This class allows to create custom block RNN modules that can later be used with Darts&#39;</span>
+<span class="sd">        :class:`BlockRNNModel`. It adds the backbone that is required to be used with Darts&#39;</span>
+<span class="sd">        :class:`TorchForecastingModel` and :class:`BlockRNNModel`.</span>
 
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;PyTorch module implementing a block RNN to be used in `BlockRNNModel`.</span>
+<span class="sd">        To create a new module, subclass from :class:`CustomBlockRNNModule` and:</span>
 
-<span class="sd">        PyTorch module implementing a simple block RNN with the specified `name` layer.</span>
-<span class="sd">        This module combines a PyTorch RNN module, together with a fully connected network, which maps the</span>
-<span class="sd">        last hidden layers to output of the desired size `output_chunk_length` and makes it compatible with</span>
-<span class="sd">        `BlockRNNModel`s.</span>
+<span class="sd">        * Define the architecture in the module constructor (`__init__()`)</span>
 
-<span class="sd">        This module uses an RNN to encode the input sequence, and subsequently uses a fully connected</span>
-<span class="sd">        network as the decoder which takes as input the last hidden state of the encoder RNN.</span>
-<span class="sd">        The final output of the decoder is a sequence of length `output_chunk_length`. In this sense,</span>
-<span class="sd">        the `_BlockRNNModule` produces &#39;blocks&#39; of forecasts at a time (which is different</span>
-<span class="sd">        from `_RNNModule` used by the `RNNModel`).</span>
+<span class="sd">        * Add the `forward()` method and define the logic of your module&#39;s forward pass</span>
+
+<span class="sd">        * Use the custom module class when creating a new :class:`BlockRNNModel` with parameter `model`.</span>
+
+<span class="sd">        You can use `darts.models.forecasting.block_rnn_model._BlockRNNModule` as an example.</span>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
-<span class="sd">        name</span>
-<span class="sd">            The name of the specific PyTorch RNN module (&quot;RNN&quot;, &quot;GRU&quot; or &quot;LSTM&quot;).</span>
 <span class="sd">        input_size</span>
 <span class="sd">            The dimensionality of the input time series.</span>
 <span class="sd">        hidden_dim</span>
@@ -234,6 +232,67 @@ <h1>Source code for darts.models.forecasting.block_rnn_model</h1><div class="hig
 <span class="sd">            The fraction of neurons that are dropped in all-but-last RNN layers.</span>
 <span class="sd">        **kwargs</span>
 <span class="sd">            all parameters required for :class:`darts.model.forecasting_models.PLForecastingModule` base class.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="c1"># Defining parameters</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_size</span> <span class="o">=</span> <span class="n">input_size</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">hidden_dim</span> <span class="o">=</span> <span class="n">hidden_dim</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_layers</span> <span class="o">=</span> <span class="n">num_layers</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">target_size</span> <span class="o">=</span> <span class="n">target_size</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">nr_params</span> <span class="o">=</span> <span class="n">nr_params</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_layers_out_fc</span> <span class="o">=</span> <span class="p">[]</span> <span class="k">if</span> <span class="n">num_layers_out_fc</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">num_layers_out_fc</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">dropout</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">out_len</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_chunk_length</span>
+
+<div class="viewcode-block" id="CustomBlockRNNModule.forward"><a class="viewcode-back" href="../../../../generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.CustomBlockRNNModule.forward">[docs]</a>    <span class="nd">@io_processor</span>
+    <span class="nd">@abstractmethod</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x_in</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;BlockRNN Module forward.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x_in</span>
+<span class="sd">            Tuple of Tensors containing the features of the input sequence. The tuple has elements</span>
+<span class="sd">            (past target, historic future covariates, future covariates, static covariates).</span>
+<span class="sd">            The shape of the past target is `(batch_size, input_length, input_size)`.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        torch.Tensor</span>
+<span class="sd">            The BlockRNN output Tensor with shape `(batch_size, output_chunk_length, target_size, nr_params)`.</span>
+<span class="sd">            It contains the prediction at the last time step of the sequence.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">pass</span></div></div>
+
+
+<span class="c1"># TODO add batch norm</span>
+<span class="k">class</span> <span class="nc">_BlockRNNModule</span><span class="p">(</span><span class="n">CustomBlockRNNModule</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">):</span>
+
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;PyTorch module implementing a block RNN to be used in `BlockRNNModel`.</span>
+
+<span class="sd">        PyTorch module implementing a simple block RNN with the specified `name` layer.</span>
+<span class="sd">        This module combines a PyTorch RNN module, together with a fully connected network, which maps the</span>
+<span class="sd">        last hidden layers to output of the desired size `output_chunk_length` and makes it compatible with</span>
+<span class="sd">        `BlockRNNModel`s.</span>
+
+<span class="sd">        This module uses an RNN to encode the input sequence, and subsequently uses a fully connected</span>
+<span class="sd">        network as the decoder which takes as input the last hidden state of the encoder RNN.</span>
+<span class="sd">        The final output of the decoder is a sequence of length `output_chunk_length`. In this sense,</span>
+<span class="sd">        the `_BlockRNNModule` produces &#39;blocks&#39; of forecasts at a time (which is different</span>
+<span class="sd">        from `_RNNModule` used by the `RNNModel`).</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        name</span>
+<span class="sd">            The name of the specific PyTorch RNN module (&quot;RNN&quot;, &quot;GRU&quot; or &quot;LSTM&quot;).</span>
+<span class="sd">        **kwargs</span>
+<span class="sd">            all parameters required for the :class:`darts.model.forecasting_models.CustomBlockRNNModule` base class.</span>
 
 <span class="sd">        Inputs</span>
 <span class="sd">        ------</span>
@@ -248,25 +307,24 @@ <h1>Source code for darts.models.forecasting.block_rnn_model</h1><div class="hig
 
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 
-        <span class="c1"># Defining parameters</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">hidden_dim</span> <span class="o">=</span> <span class="n">hidden_dim</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_layers</span> <span class="o">=</span> <span class="n">num_layers</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">target_size</span> <span class="o">=</span> <span class="n">target_size</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">nr_params</span> <span class="o">=</span> <span class="n">nr_params</span>
-        <span class="n">num_layers_out_fc</span> <span class="o">=</span> <span class="p">[]</span> <span class="k">if</span> <span class="n">num_layers_out_fc</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">num_layers_out_fc</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">out_len</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_chunk_length</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
 
         <span class="c1"># Defining the RNN module</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">rnn</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">nn</span><span class="p">,</span> <span class="n">name</span><span class="p">)(</span>
-            <span class="n">input_size</span><span class="p">,</span> <span class="n">hidden_dim</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">,</span> <span class="n">batch_first</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="n">dropout</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rnn</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">nn</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">)(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">input_size</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">hidden_dim</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">num_layers</span><span class="p">,</span>
+            <span class="n">batch_first</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">dropout</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">,</span>
         <span class="p">)</span>
 
         <span class="c1"># The RNN module is followed by a fully connected layer, which maps the last hidden layer</span>
         <span class="c1"># to the output of desired length</span>
-        <span class="n">last</span> <span class="o">=</span> <span class="n">hidden_dim</span>
+        <span class="n">last</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_dim</span>
         <span class="n">feats</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">feature</span> <span class="ow">in</span> <span class="n">num_layers_out_fc</span> <span class="o">+</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">out_len</span> <span class="o">*</span> <span class="n">target_size</span> <span class="o">*</span> <span class="n">nr_params</span><span class="p">]:</span>
+        <span class="k">for</span> <span class="n">feature</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_layers_out_fc</span> <span class="o">+</span> <span class="p">[</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">out_len</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_size</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">nr_params</span>
+        <span class="p">]:</span>
             <span class="n">feats</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">last</span><span class="p">,</span> <span class="n">feature</span><span class="p">))</span>
             <span class="n">last</span> <span class="o">=</span> <span class="n">feature</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">fc</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="n">feats</span><span class="p">)</span>
@@ -298,7 +356,7 @@ <h1>Source code for darts.models.forecasting.block_rnn_model</h1><div class="hig
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">input_chunk_length</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
         <span class="n">output_chunk_length</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
-        <span class="n">model</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;RNN&quot;</span><span class="p">,</span>
+        <span class="n">model</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Type</span><span class="p">[</span><span class="n">CustomBlockRNNModule</span><span class="p">]]</span> <span class="o">=</span> <span class="s2">&quot;RNN&quot;</span><span class="p">,</span>
         <span class="n">hidden_dim</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">25</span><span class="p">,</span>
         <span class="n">n_rnn_layers</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
         <span class="n">hidden_fc_sizes</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -335,9 +393,8 @@ <h1>Source code for darts.models.forecasting.block_rnn_model</h1><div class="hig
 <span class="sd">            the model from using future values of past and / or future covariates for prediction (depending on the</span>
 <span class="sd">            model&#39;s covariate support).</span>
 <span class="sd">        model</span>
-<span class="sd">            Either a string specifying the RNN module type (&quot;RNN&quot;, &quot;LSTM&quot; or &quot;GRU&quot;),</span>
-<span class="sd">            or a PyTorch module with the same specifications as</span>
-<span class="sd">            :class:`darts.models.block_rnn_model._BlockRNNModule`.</span>
+<span class="sd">            Either a string specifying the RNN module type (&quot;RNN&quot;, &quot;LSTM&quot; or &quot;GRU&quot;), or a subclass of</span>
+<span class="sd">            :class:`CustomBlockRNNModule` (the class itself, not an object of the class) with a custom logic.</span>
 <span class="sd">        hidden_dim</span>
 <span class="sd">            Size for feature maps for each hidden RNN layer (:math:`h_n`).</span>
 <span class="sd">            In Darts version &lt;= 0.21, hidden_dim was referred as hidden_size.</span>
@@ -398,7 +455,7 @@ <h1>Source code for darts.models.forecasting.block_rnn_model</h1><div class="hig
 <span class="sd">            If set to ``True``, any previously-existing model with the same name will be reset (all checkpoints will</span>
 <span class="sd">            be discarded). Default: ``False``.</span>
 <span class="sd">        save_checkpoints</span>
-<span class="sd">            Whether or not to automatically save the untrained model and checkpoints from training.</span>
+<span class="sd">            Whether to automatically save the untrained model and checkpoints from training.</span>
 <span class="sd">            To load the model from checkpoint, call :func:`MyModelClass.load_from_checkpoint()`, where</span>
 <span class="sd">            :class:`MyModelClass` is the :class:`TorchForecastingModel` class that was used (such as :class:`TFTModel`,</span>
 <span class="sd">            :class:`NBEATSModel`, etc.). If set to ``False``, the model can still be manually saved using</span>
@@ -443,7 +500,6 @@ <h1>Source code for darts.models.forecasting.block_rnn_model</h1><div class="hig
 <span class="sd">            &quot;devices&quot;, and &quot;auto_select_gpus&quot;``. Some examples for setting the devices inside the ``pl_trainer_kwargs``</span>
 <span class="sd">            dict:</span>
 
-
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;cpu&quot;}`` for CPU,</span>
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;gpu&quot;, &quot;devices&quot;: [i]}`` to use only GPU ``i`` (``i`` must be an integer),</span>
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;gpu&quot;, &quot;devices&quot;: -1, &quot;auto_select_gpus&quot;: True}`` to use all available GPUS.</span>
@@ -524,14 +580,16 @@ <h1>Source code for darts.models.forecasting.block_rnn_model</h1><div class="hig
 
         <span class="c1"># check we got right model type specified:</span>
         <span class="k">if</span> <span class="n">model</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;RNN&quot;</span><span class="p">,</span> <span class="s2">&quot;LSTM&quot;</span><span class="p">,</span> <span class="s2">&quot;GRU&quot;</span><span class="p">]:</span>
-            <span class="n">raise_if_not</span><span class="p">(</span>
-                <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">),</span>
-                <span class="s1">&#39;</span><span class="si">{}</span><span class="s1"> is not a valid RNN model.</span><span class="se">\n</span><span class="s1"> Please specify &quot;RNN&quot;, &quot;LSTM&quot;, &#39;</span>
-                <span class="s1">&#39;&quot;GRU&quot;, or give your own PyTorch nn.Module&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                    <span class="n">model</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span>
-                <span class="p">),</span>
-                <span class="n">logger</span><span class="p">,</span>
-            <span class="p">)</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">inspect</span><span class="o">.</span><span class="n">isclass</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span> <span class="nb">issubclass</span><span class="p">(</span>
+                <span class="n">model</span><span class="p">,</span> <span class="n">CustomBlockRNNModule</span>
+            <span class="p">):</span>
+                <span class="n">raise_log</span><span class="p">(</span>
+                    <span class="ne">ValueError</span><span class="p">(</span>
+                        <span class="s2">&quot;`model` is not a valid RNN model. Please specify &#39;RNN&#39;, &#39;LSTM&#39;, &#39;GRU&#39;, or give a subclass &quot;</span>
+                        <span class="s2">&quot;(not an instance) of darts.models.forecasting.rnn_model.CustomBlockRNNModule.&quot;</span>
+                    <span class="p">),</span>
+                    <span class="n">logger</span><span class="o">=</span><span class="n">logger</span><span class="p">,</span>
+                <span class="p">)</span>
 
         <span class="bp">self</span><span class="o">.</span><span class="n">rnn_type_or_module</span> <span class="o">=</span> <span class="n">model</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">hidden_fc_sizes</span> <span class="o">=</span> <span class="n">hidden_fc_sizes</span>
@@ -551,24 +609,25 @@ <h1>Source code for darts.models.forecasting.block_rnn_model</h1><div class="hig
         <span class="n">output_dim</span> <span class="o">=</span> <span class="n">train_sample</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
         <span class="n">nr_params</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">likelihood</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">likelihood</span><span class="o">.</span><span class="n">num_parameters</span>
 
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">rnn_type_or_module</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;RNN&quot;</span><span class="p">,</span> <span class="s2">&quot;LSTM&quot;</span><span class="p">,</span> <span class="s2">&quot;GRU&quot;</span><span class="p">]:</span>
-            <span class="n">hidden_fc_sizes</span> <span class="o">=</span> <span class="p">(</span>
-                <span class="p">[]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_fc_sizes</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_fc_sizes</span>
-            <span class="p">)</span>
-            <span class="n">model</span> <span class="o">=</span> <span class="n">_BlockRNNModule</span><span class="p">(</span>
-                <span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">rnn_type_or_module</span><span class="p">,</span>
-                <span class="n">input_size</span><span class="o">=</span><span class="n">input_dim</span><span class="p">,</span>
-                <span class="n">target_size</span><span class="o">=</span><span class="n">output_dim</span><span class="p">,</span>
-                <span class="n">nr_params</span><span class="o">=</span><span class="n">nr_params</span><span class="p">,</span>
-                <span class="n">hidden_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden_dim</span><span class="p">,</span>
-                <span class="n">num_layers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_rnn_layers</span><span class="p">,</span>
-                <span class="n">num_layers_out_fc</span><span class="o">=</span><span class="n">hidden_fc_sizes</span><span class="p">,</span>
-                <span class="n">dropout</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">,</span>
-                <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">pl_module_params</span><span class="p">,</span>
-            <span class="p">)</span>
+        <span class="n">hidden_fc_sizes</span> <span class="o">=</span> <span class="p">[]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_fc_sizes</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_fc_sizes</span>
+
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rnn_type_or_module</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+            <span class="n">model_cls</span> <span class="o">=</span> <span class="n">_BlockRNNModule</span>
+            <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rnn_type_or_module</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rnn_type_or_module</span>
-        <span class="k">return</span> <span class="n">model</span></div>
+            <span class="n">model_cls</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rnn_type_or_module</span>
+        <span class="k">return</span> <span class="n">model_cls</span><span class="p">(</span>
+            <span class="n">input_size</span><span class="o">=</span><span class="n">input_dim</span><span class="p">,</span>
+            <span class="n">target_size</span><span class="o">=</span><span class="n">output_dim</span><span class="p">,</span>
+            <span class="n">nr_params</span><span class="o">=</span><span class="n">nr_params</span><span class="p">,</span>
+            <span class="n">hidden_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden_dim</span><span class="p">,</span>
+            <span class="n">num_layers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_rnn_layers</span><span class="p">,</span>
+            <span class="n">num_layers_out_fc</span><span class="o">=</span><span class="n">hidden_fc_sizes</span><span class="p">,</span>
+            <span class="n">dropout</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">,</span>
+            <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">pl_module_params</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">)</span></div>
 </pre></div>
 
               </div>
diff --git a/_modules/darts/models/forecasting/dlinear.html b/_modules/darts/models/forecasting/dlinear.html
index aa759fd33a..52ee986fb4 100644
--- a/_modules/darts/models/forecasting/dlinear.html
+++ b/_modules/darts/models/forecasting/dlinear.html
@@ -495,7 +495,7 @@ <h1>Source code for darts.models.forecasting.dlinear</h1><div class="highlight">
 <span class="sd">            If set to ``True``, any previously-existing model with the same name will be reset (all checkpoints will</span>
 <span class="sd">            be discarded). Default: ``False``.</span>
 <span class="sd">        save_checkpoints</span>
-<span class="sd">            Whether or not to automatically save the untrained model and checkpoints from training.</span>
+<span class="sd">            Whether to automatically save the untrained model and checkpoints from training.</span>
 <span class="sd">            To load the model from checkpoint, call :func:`MyModelClass.load_from_checkpoint()`, where</span>
 <span class="sd">            :class:`MyModelClass` is the :class:`TorchForecastingModel` class that was used (such as :class:`TFTModel`,</span>
 <span class="sd">            :class:`NBEATSModel`, etc.). If set to ``False``, the model can still be manually saved using</span>
@@ -540,7 +540,6 @@ <h1>Source code for darts.models.forecasting.dlinear</h1><div class="highlight">
 <span class="sd">            &quot;devices&quot;, and &quot;auto_select_gpus&quot;``. Some examples for setting the devices inside the ``pl_trainer_kwargs``</span>
 <span class="sd">            dict:</span>
 
-
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;cpu&quot;}`` for CPU,</span>
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;gpu&quot;, &quot;devices&quot;: [i]}`` to use only GPU ``i`` (``i`` must be an integer),</span>
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;gpu&quot;, &quot;devices&quot;: -1, &quot;auto_select_gpus&quot;: True}`` to use all available GPUS.</span>
diff --git a/_modules/darts/models/forecasting/exponential_smoothing.html b/_modules/darts/models/forecasting/exponential_smoothing.html
index 4ff069e25c..46e2dd2450 100644
--- a/_modules/darts/models/forecasting/exponential_smoothing.html
+++ b/_modules/darts/models/forecasting/exponential_smoothing.html
@@ -304,7 +304,13 @@ <h1>Source code for darts.models.forecasting.exponential_smoothing</h1><div clas
 
         <span class="k">return</span> <span class="bp">self</span></div>
 
-<div class="viewcode-block" id="ExponentialSmoothing.predict"><a class="viewcode-back" href="../../../../generated_api/darts.models.forecasting.exponential_smoothing.html#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.predict">[docs]</a>    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">num_samples</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+<div class="viewcode-block" id="ExponentialSmoothing.predict"><a class="viewcode-back" href="../../../../generated_api/darts.models.forecasting.exponential_smoothing.html#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.predict">[docs]</a>    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">num_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+        <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">show_warnings</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">)</span>
 
         <span class="k">if</span> <span class="n">num_samples</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
diff --git a/_modules/darts/models/forecasting/fft.html b/_modules/darts/models/forecasting/fft.html
index 62d38d793e..68adb80184 100644
--- a/_modules/darts/models/forecasting/fft.html
+++ b/_modules/darts/models/forecasting/fft.html
@@ -533,7 +533,13 @@ <h1>Source code for darts.models.forecasting.fft</h1><div class="highlight"><pre
 
         <span class="k">return</span> <span class="bp">self</span></div>
 
-<div class="viewcode-block" id="FFT.predict"><a class="viewcode-back" href="../../../../generated_api/darts.models.forecasting.fft.html#darts.models.forecasting.fft.FFT.predict">[docs]</a>    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+<div class="viewcode-block" id="FFT.predict"><a class="viewcode-back" href="../../../../generated_api/darts.models.forecasting.fft.html#darts.models.forecasting.fft.FFT.predict">[docs]</a>    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">num_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+        <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">show_warnings</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">)</span>
         <span class="n">trend_forecast</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
             <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">trend_function</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">training_series</span><span class="p">))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)]</span>
diff --git a/_modules/darts/models/forecasting/forecasting_model.html b/_modules/darts/models/forecasting/forecasting_model.html
index 25cb9c301a..158824f99a 100644
--- a/_modules/darts/models/forecasting/forecasting_model.html
+++ b/_modules/darts/models/forecasting/forecasting_model.html
@@ -402,6 +402,14 @@ <h1>Source code for darts.models.forecasting.forecasting_model</h1><div class="h
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;likelihood&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
 
+    <span class="nd">@property</span>
+    <span class="nd">@abstractmethod</span>
+    <span class="k">def</span> <span class="nf">supports_transferrable_series_prediction</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Whether the model supports prediction for any input `series`.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">pass</span>
+
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">uses_past_covariates</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -445,7 +453,13 @@ <h1>Source code for darts.models.forecasting.forecasting_model</h1><div class="h
         <span class="k">return</span> <span class="kc">None</span>
 
     <span class="nd">@abstractmethod</span>
-    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TimeSeries</span><span class="p">:</span>
+    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">num_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+        <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">show_warnings</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TimeSeries</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Forecasts values for `n` time steps after the end of the training series.</span>
 
 <span class="sd">        Parameters</span>
@@ -455,6 +469,10 @@ <h1>Source code for darts.models.forecasting.forecasting_model</h1><div class="h
 <span class="sd">        num_samples</span>
 <span class="sd">            Number of times a prediction is sampled from a probabilistic model. Should be left set to 1</span>
 <span class="sd">            for deterministic models.</span>
+<span class="sd">        verbose</span>
+<span class="sd">            Optionally, set the prediction verbosity. Not effective for all models.</span>
+<span class="sd">        show_warnings</span>
+<span class="sd">            Optionally, control whether warnings are shown. Not effective for all models.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -482,49 +500,59 @@ <h1>Source code for darts.models.forecasting.forecasting_model</h1><div class="h
     <span class="k">def</span> <span class="nf">_fit_wrapper</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">series</span><span class="p">:</span> <span class="n">TimeSeries</span><span class="p">,</span>
-        <span class="n">past_covariates</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">TimeSeries</span><span class="p">],</span>
-        <span class="n">future_covariates</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">TimeSeries</span><span class="p">],</span>
+        <span class="n">past_covariates</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">TimeSeries</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">future_covariates</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">TimeSeries</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
         <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
     <span class="p">):</span>
-        <span class="n">supported_params</span> <span class="o">=</span> <span class="n">inspect</span><span class="o">.</span><span class="n">signature</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fit</span><span class="p">)</span><span class="o">.</span><span class="n">parameters</span>
-        <span class="n">kwargs_</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">supported_params</span><span class="p">}</span>
-
+        <span class="n">add_kwargs</span> <span class="o">=</span> <span class="p">{}</span>
         <span class="c1"># handle past and future covariates based on model support</span>
-        <span class="k">for</span> <span class="n">covs</span><span class="p">,</span> <span class="n">name</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">([</span><span class="n">past_covariates</span><span class="p">,</span> <span class="n">future_covariates</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;past&quot;</span><span class="p">,</span> <span class="s2">&quot;future&quot;</span><span class="p">]):</span>
-            <span class="n">covs_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">_covariates&quot;</span>
+        <span class="k">for</span> <span class="n">covs</span><span class="p">,</span> <span class="n">covs_name</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
+            <span class="p">[</span><span class="n">past_covariates</span><span class="p">,</span> <span class="n">future_covariates</span><span class="p">],</span>
+            <span class="p">[</span><span class="s2">&quot;past_covariates&quot;</span><span class="p">,</span> <span class="s2">&quot;future_covariates&quot;</span><span class="p">],</span>
+        <span class="p">):</span>
             <span class="k">if</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;supports_</span><span class="si">{</span><span class="n">covs_name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">):</span>
-                <span class="n">kwargs_</span><span class="p">[</span><span class="n">covs_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">covs</span>
+                <span class="n">add_kwargs</span><span class="p">[</span><span class="n">covs_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">covs</span>
             <span class="k">elif</span> <span class="n">covs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">raise_log</span><span class="p">(</span>
                     <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Model cannot be fit/trained with `</span><span class="si">{</span><span class="n">covs_name</span><span class="si">}</span><span class="s2">`.&quot;</span><span class="p">),</span>
                     <span class="n">logger</span><span class="p">,</span>
                 <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">series</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs_</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">series</span><span class="o">=</span><span class="n">series</span><span class="p">,</span> <span class="o">**</span><span class="n">add_kwargs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 
     <span class="k">def</span> <span class="nf">_predict_wrapper</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">series</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">TimeSeries</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">past_covariates</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">TimeSeries</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">future_covariates</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">TimeSeries</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">predict_likelihood_parameters</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
         <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">TimeSeries</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">TimeSeries</span><span class="p">]]:</span>
-        <span class="n">supported_params</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">inspect</span><span class="o">.</span><span class="n">signature</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">predict</span><span class="p">)</span><span class="o">.</span><span class="n">parameters</span><span class="p">)</span>
-
-        <span class="c1"># if predict() accepts covariates, the model might not support them at inference</span>
-        <span class="k">for</span> <span class="n">covs_name</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;past_covariates&quot;</span><span class="p">,</span> <span class="s2">&quot;future_covariates&quot;</span><span class="p">]:</span>
-            <span class="k">if</span> <span class="n">covs_name</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;supports_</span><span class="si">{</span><span class="n">covs_name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">):</span>
-                <span class="k">if</span> <span class="n">kwargs</span><span class="p">[</span><span class="n">covs_name</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">supported_params</span> <span class="o">=</span> <span class="n">supported_params</span> <span class="o">-</span> <span class="p">{</span><span class="n">covs_name</span><span class="p">}</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">raise_log</span><span class="p">(</span>
-                        <span class="ne">ValueError</span><span class="p">(</span>
-                            <span class="sa">f</span><span class="s2">&quot;Model prediction does not support `</span><span class="si">{</span><span class="n">covs_name</span><span class="si">}</span><span class="s2">`, either because it &quot;</span>
-                            <span class="sa">f</span><span class="s2">&quot;does not support `</span><span class="si">{</span><span class="n">covs_name</span><span class="si">}</span><span class="s2">` in general, or because it was fit/trained &quot;</span>
-                            <span class="sa">f</span><span class="s2">&quot;without using `</span><span class="si">{</span><span class="n">covs_name</span><span class="si">}</span><span class="s2">`.&quot;</span>
-                        <span class="p">),</span>
-                        <span class="n">logger</span><span class="p">,</span>
-                    <span class="p">)</span>
+        <span class="n">add_kwargs</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="c1"># not all models supports input `series` at inference</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">supports_transferrable_series_prediction</span><span class="p">:</span>
+            <span class="n">add_kwargs</span><span class="p">[</span><span class="s2">&quot;series&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">series</span>
+
+        <span class="c1"># even if predict() accepts covariates, the model might not support them at inference</span>
+        <span class="k">for</span> <span class="n">covs</span><span class="p">,</span> <span class="n">name</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
+            <span class="p">[</span><span class="n">past_covariates</span><span class="p">,</span> <span class="n">future_covariates</span><span class="p">],</span>
+            <span class="p">[</span><span class="s2">&quot;past_covariates&quot;</span><span class="p">,</span> <span class="s2">&quot;future_covariates&quot;</span><span class="p">],</span>
+        <span class="p">):</span>
+            <span class="k">if</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;supports_</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">):</span>
+                <span class="n">add_kwargs</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">covs</span>
+            <span class="k">elif</span> <span class="n">covs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">raise_log</span><span class="p">(</span>
+                    <span class="ne">ValueError</span><span class="p">(</span>
+                        <span class="sa">f</span><span class="s2">&quot;Model prediction does not support `</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">`, either because it &quot;</span>
+                        <span class="sa">f</span><span class="s2">&quot;does not support `</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">` in general, or because it was fit/trained &quot;</span>
+                        <span class="sa">f</span><span class="s2">&quot;without using `</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">`.&quot;</span>
+                    <span class="p">),</span>
+                    <span class="n">logger</span><span class="p">,</span>
+                <span class="p">)</span>
 
-        <span class="n">kwargs_</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">supported_params</span><span class="p">}</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs_</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">supports_likelihood_parameter_prediction</span><span class="p">:</span>
+            <span class="n">add_kwargs</span><span class="p">[</span><span class="s2">&quot;predict_likelihood_parameters&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">predict_likelihood_parameters</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="n">n</span><span class="p">,</span> <span class="o">**</span><span class="n">add_kwargs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">min_train_series_length</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
@@ -2266,6 +2294,13 @@ <h1>Source code for darts.models.forecasting.forecasting_model</h1><div class="h
         <span class="c1">#  that use an input to predict an output.</span>
         <span class="k">return</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">min_train_series_length</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span>
 
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">supports_transferrable_series_prediction</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Whether the model supports prediction for any input `series`.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="kc">False</span>
+
 
 <span class="k">class</span> <span class="nc">GlobalForecastingModel</span><span class="p">(</span><span class="n">ForecastingModel</span><span class="p">,</span> <span class="n">ABC</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;The base class for &quot;global&quot; forecasting models, handling several time series and optional covariates.</span>
@@ -2478,6 +2513,13 @@ <h1>Source code for darts.models.forecasting.forecasting_model</h1><div class="h
     <span class="k">def</span> <span class="nf">supports_optimized_historical_forecasts</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Whether the model supports optimized historical forecasts</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="kc">True</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">supports_transferrable_series_prediction</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Whether the model supports prediction for any input `series`.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="kc">True</span>
 
@@ -2587,6 +2629,8 @@ <h1>Source code for darts.models.forecasting.forecasting_model</h1><div class="h
         <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
         <span class="n">future_covariates</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">TimeSeries</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
         <span class="n">num_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+        <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">show_warnings</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
         <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TimeSeries</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Forecasts values for `n` time steps after the end of the training series.</span>
@@ -2604,6 +2648,10 @@ <h1>Source code for darts.models.forecasting.forecasting_model</h1><div class="h
 <span class="sd">        num_samples</span>
 <span class="sd">            Number of times a prediction is sampled from a probabilistic model. Should be left set to 1</span>
 <span class="sd">            for deterministic models.</span>
+<span class="sd">        verbose</span>
+<span class="sd">            Optionally, set the prediction verbosity. Not effective for all models.</span>
+<span class="sd">        show_warnings</span>
+<span class="sd">            Optionally, control whether warnings are shown. Not effective for all models.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -2747,6 +2795,8 @@ <h1>Source code for darts.models.forecasting.forecasting_model</h1><div class="h
         <span class="n">series</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">TimeSeries</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
         <span class="n">future_covariates</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">TimeSeries</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
         <span class="n">num_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+        <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">show_warnings</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
         <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TimeSeries</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;If the `series` parameter is not set, forecasts values for `n` time steps after the end of the training</span>
@@ -2772,6 +2822,10 @@ <h1>Source code for darts.models.forecasting.forecasting_model</h1><div class="h
 <span class="sd">        num_samples</span>
 <span class="sd">            Number of times a prediction is sampled from a probabilistic model. Should be left set to 1</span>
 <span class="sd">            for deterministic models.</span>
+<span class="sd">        verbose</span>
+<span class="sd">            Optionally, set the prediction verbosity. Not effective for all models.</span>
+<span class="sd">        show_warnings</span>
+<span class="sd">            Optionally, control whether warnings are shown. Not effective for all models.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -2866,6 +2920,13 @@ <h1>Source code for darts.models.forecasting.forecasting_model</h1><div class="h
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">pass</span>
 
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">supports_transferrable_series_prediction</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Whether the model supports prediction for any input `series`.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="kc">True</span>
+
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">_supports_non_retrainable_historical_forecasts</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
         <span class="k">return</span> <span class="kc">True</span>
diff --git a/_modules/darts/models/forecasting/kalman_forecaster.html b/_modules/darts/models/forecasting/kalman_forecaster.html
index e4ac4c5247..5dfc0157f6 100644
--- a/_modules/darts/models/forecasting/kalman_forecaster.html
+++ b/_modules/darts/models/forecasting/kalman_forecaster.html
@@ -291,6 +291,8 @@ <h1>Source code for darts.models.forecasting.kalman_forecaster</h1><div class="h
         <span class="n">series</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">TimeSeries</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
         <span class="n">future_covariates</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">TimeSeries</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
         <span class="n">num_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+        <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">show_warnings</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
         <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TimeSeries</span><span class="p">:</span>
         <span class="c1"># we override `predict()` to pass a non-None `series`, so that historic_future_covariates</span>
diff --git a/_modules/darts/models/forecasting/nbeats.html b/_modules/darts/models/forecasting/nbeats.html
index 92242a18e9..b7ae872e8f 100644
--- a/_modules/darts/models/forecasting/nbeats.html
+++ b/_modules/darts/models/forecasting/nbeats.html
@@ -820,7 +820,7 @@ <h1>Source code for darts.models.forecasting.nbeats</h1><div class="highlight"><
 <span class="sd">            If set to ``True``, any previously-existing model with the same name will be reset (all checkpoints will</span>
 <span class="sd">            be discarded). Default: ``False``.</span>
 <span class="sd">        save_checkpoints</span>
-<span class="sd">            Whether or not to automatically save the untrained model and checkpoints from training.</span>
+<span class="sd">            Whether to automatically save the untrained model and checkpoints from training.</span>
 <span class="sd">            To load the model from checkpoint, call :func:`MyModelClass.load_from_checkpoint()`, where</span>
 <span class="sd">            :class:`MyModelClass` is the :class:`TorchForecastingModel` class that was used (such as :class:`TFTModel`,</span>
 <span class="sd">            :class:`NBEATSModel`, etc.). If set to ``False``, the model can still be manually saved using</span>
@@ -865,7 +865,6 @@ <h1>Source code for darts.models.forecasting.nbeats</h1><div class="highlight"><
 <span class="sd">            &quot;devices&quot;, and &quot;auto_select_gpus&quot;``. Some examples for setting the devices inside the ``pl_trainer_kwargs``</span>
 <span class="sd">            dict:</span>
 
-
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;cpu&quot;}`` for CPU,</span>
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;gpu&quot;, &quot;devices&quot;: [i]}`` to use only GPU ``i`` (``i`` must be an integer),</span>
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;gpu&quot;, &quot;devices&quot;: -1, &quot;auto_select_gpus&quot;: True}`` to use all available GPUS.</span>
diff --git a/_modules/darts/models/forecasting/nhits.html b/_modules/darts/models/forecasting/nhits.html
index 1254519e79..1d61656453 100644
--- a/_modules/darts/models/forecasting/nhits.html
+++ b/_modules/darts/models/forecasting/nhits.html
@@ -756,7 +756,7 @@ <h1>Source code for darts.models.forecasting.nhits</h1><div class="highlight"><p
 <span class="sd">            If set to ``True``, any previously-existing model with the same name will be reset (all checkpoints will</span>
 <span class="sd">            be discarded). Default: ``False``.</span>
 <span class="sd">        save_checkpoints</span>
-<span class="sd">            Whether or not to automatically save the untrained model and checkpoints from training.</span>
+<span class="sd">            Whether to automatically save the untrained model and checkpoints from training.</span>
 <span class="sd">            To load the model from checkpoint, call :func:`MyModelClass.load_from_checkpoint()`, where</span>
 <span class="sd">            :class:`MyModelClass` is the :class:`TorchForecastingModel` class that was used (such as :class:`TFTModel`,</span>
 <span class="sd">            :class:`NBEATSModel`, etc.). If set to ``False``, the model can still be manually saved using</span>
@@ -801,7 +801,6 @@ <h1>Source code for darts.models.forecasting.nhits</h1><div class="highlight"><p
 <span class="sd">            &quot;devices&quot;, and &quot;auto_select_gpus&quot;``. Some examples for setting the devices inside the ``pl_trainer_kwargs``</span>
 <span class="sd">            dict:</span>
 
-
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;cpu&quot;}`` for CPU,</span>
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;gpu&quot;, &quot;devices&quot;: [i]}`` to use only GPU ``i`` (``i`` must be an integer),</span>
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;gpu&quot;, &quot;devices&quot;: -1, &quot;auto_select_gpus&quot;: True}`` to use all available GPUS.</span>
diff --git a/_modules/darts/models/forecasting/nlinear.html b/_modules/darts/models/forecasting/nlinear.html
index b2af9f47d0..e58f868276 100644
--- a/_modules/darts/models/forecasting/nlinear.html
+++ b/_modules/darts/models/forecasting/nlinear.html
@@ -449,7 +449,7 @@ <h1>Source code for darts.models.forecasting.nlinear</h1><div class="highlight">
 <span class="sd">            If set to ``True``, any previously-existing model with the same name will be reset (all checkpoints will</span>
 <span class="sd">            be discarded). Default: ``False``.</span>
 <span class="sd">        save_checkpoints</span>
-<span class="sd">            Whether or not to automatically save the untrained model and checkpoints from training.</span>
+<span class="sd">            Whether to automatically save the untrained model and checkpoints from training.</span>
 <span class="sd">            To load the model from checkpoint, call :func:`MyModelClass.load_from_checkpoint()`, where</span>
 <span class="sd">            :class:`MyModelClass` is the :class:`TorchForecastingModel` class that was used (such as :class:`TFTModel`,</span>
 <span class="sd">            :class:`NBEATSModel`, etc.). If set to ``False``, the model can still be manually saved using</span>
diff --git a/_modules/darts/models/forecasting/pl_forecasting_module.html b/_modules/darts/models/forecasting/pl_forecasting_module.html
index 4acd5c184c..4fe26f9b3b 100644
--- a/_modules/darts/models/forecasting/pl_forecasting_module.html
+++ b/_modules/darts/models/forecasting/pl_forecasting_module.html
@@ -170,6 +170,7 @@ <h1>Source code for darts.models.forecasting.pl_forecasting_module</h1><div clas
 <span class="sd">&quot;&quot;&quot;</span>
 
 <span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABC</span><span class="p">,</span> <span class="n">abstractmethod</span>
+<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">wraps</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span>
 
 <span class="kn">import</span> <span class="nn">pytorch_lightning</span> <span class="k">as</span> <span class="nn">pl</span>
@@ -209,6 +210,7 @@ <h1>Source code for darts.models.forecasting.pl_forecasting_module</h1><div clas
 <span class="sd">        normalizes batch input target features, and inverse transform the forward output back to the original scale</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
+    <span class="nd">@wraps</span><span class="p">(</span><span class="n">forward</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">forward_wrapper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_reversible_instance_norm</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
diff --git a/_modules/darts/models/forecasting/regression_model.html b/_modules/darts/models/forecasting/regression_model.html
index a8aaaabafe..c6bfd095b2 100644
--- a/_modules/darts/models/forecasting/regression_model.html
+++ b/_modules/darts/models/forecasting/regression_model.html
@@ -939,6 +939,8 @@ <h1>Source code for darts.models.forecasting.regression_model</h1><div class="hi
 <span class="sd">        **kwargs : dict, optional</span>
 <span class="sd">            Additional keyword arguments passed to the `predict` method of the model. Only works with</span>
 <span class="sd">            univariate target series.</span>
+<span class="sd">        show_warnings</span>
+<span class="sd">            Optionally, control whether warnings are shown. Not effective for all models.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="n">series</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="c1"># then there must be a single TS, and that was saved in super().fit as self.training_series</span>
diff --git a/_modules/darts/models/forecasting/rnn_model.html b/_modules/darts/models/forecasting/rnn_model.html
index 7ebf23f407..5e138ac751 100644
--- a/_modules/darts/models/forecasting/rnn_model.html
+++ b/_modules/darts/models/forecasting/rnn_model.html
@@ -170,12 +170,14 @@ <h1>Source code for darts.models.forecasting.rnn_model</h1><div class="highlight
 <span class="sd">-------------------------</span>
 <span class="sd">&quot;&quot;&quot;</span>
 
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span>
+<span class="kn">import</span> <span class="nn">inspect</span>
+<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABC</span><span class="p">,</span> <span class="n">abstractmethod</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Type</span><span class="p">,</span> <span class="n">Union</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
 
-<span class="kn">from</span> <span class="nn">darts.logging</span> <span class="kn">import</span> <span class="n">get_logger</span><span class="p">,</span> <span class="n">raise_if_not</span>
+<span class="kn">from</span> <span class="nn">darts.logging</span> <span class="kn">import</span> <span class="n">get_logger</span><span class="p">,</span> <span class="n">raise_if_not</span><span class="p">,</span> <span class="n">raise_log</span>
 <span class="kn">from</span> <span class="nn">darts.models.forecasting.pl_forecasting_module</span> <span class="kn">import</span> <span class="p">(</span>
     <span class="n">PLDualCovariatesModule</span><span class="p">,</span>
     <span class="n">io_processor</span><span class="p">,</span>
@@ -187,11 +189,9 @@ <h1>Source code for darts.models.forecasting.rnn_model</h1><div class="highlight
 <span class="n">logger</span> <span class="o">=</span> <span class="n">get_logger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
 
 
-<span class="c1"># TODO add batch norm</span>
-<span class="k">class</span> <span class="nc">_RNNModule</span><span class="p">(</span><span class="n">PLDualCovariatesModule</span><span class="p">):</span>
+<div class="viewcode-block" id="CustomRNNModule"><a class="viewcode-back" href="../../../../generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.CustomRNNModule">[docs]</a><span class="k">class</span> <span class="nc">CustomRNNModule</span><span class="p">(</span><span class="n">PLDualCovariatesModule</span><span class="p">,</span> <span class="n">ABC</span><span class="p">):</span>
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
-        <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
         <span class="n">input_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
         <span class="n">hidden_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
         <span class="n">num_layers</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -200,18 +200,22 @@ <h1>Source code for darts.models.forecasting.rnn_model</h1><div class="highlight
         <span class="n">dropout</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span>
         <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
     <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;This class allows to create custom RNN modules that can later be used with Darts&#39; :class:`RNNModel`.</span>
+<span class="sd">        It adds the backbone that is required to be used with Darts&#39; :class:`TorchForecastingModel` and</span>
+<span class="sd">        :class:`RNNModel`.</span>
 
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;PyTorch module implementing an RNN to be used in `RNNModel`.</span>
+<span class="sd">        To create a new module, subclass from :class:`CustomRNNModule` and:</span>
 
-<span class="sd">        PyTorch module implementing a simple RNN with the specified `name` type.</span>
-<span class="sd">        This module combines a PyTorch RNN module, together with one fully connected layer which</span>
-<span class="sd">        maps the hidden state of the RNN at each step to the output value of the model at that</span>
-<span class="sd">        time step.</span>
+<span class="sd">        * Define the architecture in the module constructor (`__init__()`)</span>
+
+<span class="sd">        * Add the `forward()` method and define the logic of your module&#39;s forward pass</span>
+
+<span class="sd">        * Use the custom module class when creating a new :class:`RNNModel` with parameter `model`.</span>
+
+<span class="sd">        You can use `darts.models.forecasting.rnn_model._RNNModule` as an example.</span>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
-<span class="sd">        name</span>
-<span class="sd">            The name of the specific PyTorch RNN module (&quot;RNN&quot;, &quot;GRU&quot; or &quot;LSTM&quot;).</span>
 <span class="sd">        input_size</span>
 <span class="sd">            The dimensionality of the input time series.</span>
 <span class="sd">        hidden_dim</span>
@@ -226,55 +230,43 @@ <h1>Source code for darts.models.forecasting.rnn_model</h1><div class="highlight
 <span class="sd">            The fraction of neurons that are dropped in all-but-last RNN layers.</span>
 <span class="sd">        **kwargs</span>
 <span class="sd">            all parameters required for :class:`darts.model.forecasting_models.PLForecastingModule` base class.</span>
-
-<span class="sd">        Inputs</span>
-<span class="sd">        ------</span>
-<span class="sd">        x of shape `(batch_size, input_length, input_size)`</span>
-<span class="sd">            Tensor containing the features of the input sequence. The `input_length` is not fixed.</span>
-
-<span class="sd">        Outputs</span>
-<span class="sd">        -------</span>
-<span class="sd">        y of shape `(batch_size, output_chunk_length, target_size, nr_params)`</span>
-<span class="sd">            Tensor containing the outputs of the RNN at every time step of the input sequence.</span>
-<span class="sd">            During training the whole tensor is used as output, whereas during prediction we only use y[:, -1, :].</span>
-<span class="sd">            However, this module always returns the whole Tensor.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-
         <span class="c1"># RNNModule doesn&#39;t really need input and output_chunk_length for PLModule</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 
         <span class="c1"># Defining parameters</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_size</span> <span class="o">=</span> <span class="n">input_size</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">hidden_dim</span> <span class="o">=</span> <span class="n">hidden_dim</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_layers</span> <span class="o">=</span> <span class="n">num_layers</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">target_size</span> <span class="o">=</span> <span class="n">target_size</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">nr_params</span> <span class="o">=</span> <span class="n">nr_params</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
-
-        <span class="c1"># Defining the RNN module</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">rnn</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">nn</span><span class="p">,</span> <span class="n">name</span><span class="p">)(</span>
-            <span class="n">input_size</span><span class="p">,</span> <span class="n">hidden_dim</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">,</span> <span class="n">batch_first</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="n">dropout</span>
-        <span class="p">)</span>
-
-        <span class="c1"># The RNN module needs a linear layer V that transforms hidden states into outputs, individually</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">V</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">hidden_dim</span><span class="p">,</span> <span class="n">target_size</span> <span class="o">*</span> <span class="n">nr_params</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">dropout</span>
 
-    <span class="nd">@io_processor</span>
+<div class="viewcode-block" id="CustomRNNModule.forward"><a class="viewcode-back" href="../../../../generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.CustomRNNModule.forward">[docs]</a>    <span class="nd">@io_processor</span>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">x_in</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">h</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
-        <span class="n">x</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">x_in</span>
-        <span class="c1"># data is of size (batch_size, input_length, input_size)</span>
-        <span class="n">batch_size</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="c1"># out is of size (batch_size, input_length, hidden_dim)</span>
-        <span class="n">out</span><span class="p">,</span> <span class="n">last_hidden_state</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rnn</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">if</span> <span class="n">h</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">rnn</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">h</span><span class="p">)</span>
-
-        <span class="c1"># Here, we apply the V matrix to every hidden state to produce the outputs</span>
-        <span class="n">predictions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">V</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
-
-        <span class="c1"># predictions is of size (batch_size, input_length, target_size)</span>
-        <span class="n">predictions</span> <span class="o">=</span> <span class="n">predictions</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">nr_params</span><span class="p">)</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;RNN Module forward.</span>
 
-        <span class="c1"># returns outputs for all inputs, only the last one is needed for prediction time</span>
-        <span class="k">return</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">last_hidden_state</span>
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x_in</span>
+<span class="sd">            Tuple of Tensors containing the features of the input sequence. The tuple has elements (past target,</span>
+<span class="sd">            historic future covariates, future covariates, static covariates). The shape of the past target is</span>
+<span class="sd">            `(batch_size, input_length, input_size)`.</span>
+<span class="sd">        h</span>
+<span class="sd">            Optionally, the hidden state.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Tuple[torch.Tensor, torch.Tensor]</span>
+<span class="sd">            Tuple of Tensors with elements (RNN output, hidden state). The RNN output Tensor has shape</span>
+<span class="sd">            `(batch_size, output_chunk_length, target_size, nr_params)`. It contains the outputs at every</span>
+<span class="sd">            time step of the input sequence. During training the whole tensor is used as output, whereas during</span>
+<span class="sd">            prediction we only use y[:, -1, :]. However, this module always returns the whole Tensor.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">pass</span></div>
 
     <span class="k">def</span> <span class="nf">_produce_train_output</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_batch</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
         <span class="p">(</span>
@@ -368,14 +360,85 @@ <h1>Source code for darts.models.forecasting.rnn_model</h1><div class="highlight
         <span class="c1"># bring predictions into desired format and drop unnecessary values</span>
         <span class="n">batch_prediction</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">batch_prediction</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
         <span class="n">batch_prediction</span> <span class="o">=</span> <span class="n">batch_prediction</span><span class="p">[:,</span> <span class="p">:</span><span class="n">n</span><span class="p">,</span> <span class="p">:]</span>
-        <span class="k">return</span> <span class="n">batch_prediction</span>
+        <span class="k">return</span> <span class="n">batch_prediction</span></div>
+
+
+<span class="c1"># TODO add batch norm</span>
+<span class="k">class</span> <span class="nc">_RNNModule</span><span class="p">(</span><span class="n">CustomRNNModule</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;PyTorch module implementing an RNN to be used in `RNNModel`.</span>
+
+<span class="sd">        PyTorch module implementing a simple RNN with the specified `name` type.</span>
+<span class="sd">        This module combines a PyTorch RNN module, together with one fully connected layer which</span>
+<span class="sd">        maps the hidden state of the RNN at each step to the output value of the model at that</span>
+<span class="sd">        time step.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        name</span>
+<span class="sd">            The name of the specific PyTorch RNN module (&quot;RNN&quot;, &quot;GRU&quot; or &quot;LSTM&quot;).</span>
+<span class="sd">        **kwargs</span>
+<span class="sd">            all parameters required for the :class:`darts.model.forecasting_models.CustomRNNModule` base class.</span>
+
+<span class="sd">        Inputs</span>
+<span class="sd">        ------</span>
+<span class="sd">        x of shape `(batch_size, input_length, input_size)`</span>
+<span class="sd">            Tensor containing the features of the input sequence. The `input_length` is not fixed.</span>
+
+<span class="sd">        Outputs</span>
+<span class="sd">        -------</span>
+<span class="sd">        y of shape `(batch_size, output_chunk_length, target_size, nr_params)`</span>
+<span class="sd">            Tensor containing the outputs of the RNN at every time step of the input sequence.</span>
+<span class="sd">            During training the whole tensor is used as output, whereas during prediction we only use y[:, -1, :].</span>
+<span class="sd">            However, this module always returns the whole Tensor.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="c1"># RNNModule doesn&#39;t really need input and output_chunk_length for PLModule</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
+
+        <span class="c1"># Defining the RNN module</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rnn</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">nn</span><span class="p">,</span> <span class="n">name</span><span class="p">)(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">input_size</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">hidden_dim</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">num_layers</span><span class="p">,</span>
+            <span class="n">batch_first</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">dropout</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="c1"># The RNN module needs a linear layer V that transforms hidden states into outputs, individually</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">V</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden_dim</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_size</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">nr_params</span><span class="p">)</span>
+
+    <span class="nd">@io_processor</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">x_in</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">h</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+        <span class="n">x</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">x_in</span>
+        <span class="c1"># data is of size (batch_size, input_length, input_size)</span>
+        <span class="n">batch_size</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="c1"># out is of size (batch_size, input_length, hidden_dim)</span>
+        <span class="n">out</span><span class="p">,</span> <span class="n">last_hidden_state</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rnn</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">if</span> <span class="n">h</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">rnn</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">h</span><span class="p">)</span>
+
+        <span class="c1"># Here, we apply the V matrix to every hidden state to produce the outputs</span>
+        <span class="n">predictions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">V</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
+
+        <span class="c1"># predictions is of size (batch_size, input_length, target_size)</span>
+        <span class="n">predictions</span> <span class="o">=</span> <span class="n">predictions</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">nr_params</span><span class="p">)</span>
+
+        <span class="c1"># returns outputs for all inputs, only the last one is needed for prediction time</span>
+        <span class="k">return</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">last_hidden_state</span>
 
 
 <div class="viewcode-block" id="RNNModel"><a class="viewcode-back" href="../../../../generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel">[docs]</a><span class="k">class</span> <span class="nc">RNNModel</span><span class="p">(</span><span class="n">DualCovariatesTorchModel</span><span class="p">):</span>
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">input_chunk_length</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
-        <span class="n">model</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;RNN&quot;</span><span class="p">,</span>
+        <span class="n">model</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Type</span><span class="p">[</span><span class="n">CustomRNNModule</span><span class="p">]]</span> <span class="o">=</span> <span class="s2">&quot;RNN&quot;</span><span class="p">,</span>
         <span class="n">hidden_dim</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">25</span><span class="p">,</span>
         <span class="n">n_rnn_layers</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
         <span class="n">dropout</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span>
@@ -413,9 +476,8 @@ <h1>Source code for darts.models.forecasting.rnn_model</h1><div class="highlight
 <span class="sd">        input_chunk_length</span>
 <span class="sd">            Number of past time steps that are fed to the forecasting module at prediction time.</span>
 <span class="sd">        model</span>
-<span class="sd">            Either a string specifying the RNN module type (&quot;RNN&quot;, &quot;LSTM&quot; or &quot;GRU&quot;),</span>
-<span class="sd">            or a PyTorch module with the same specifications as</span>
-<span class="sd">            `darts.models.rnn_model._RNNModule`.</span>
+<span class="sd">            Either a string specifying the RNN module type (&quot;RNN&quot;, &quot;LSTM&quot; or &quot;GRU&quot;), or a subclass of</span>
+<span class="sd">            :class:`CustomRNNModule` (the class itself, not an object of the class) with a custom logic.</span>
 <span class="sd">        hidden_dim</span>
 <span class="sd">            Size for feature maps for each hidden RNN layer (:math:`h_n`).</span>
 <span class="sd">        n_rnn_layers</span>
@@ -475,7 +537,7 @@ <h1>Source code for darts.models.forecasting.rnn_model</h1><div class="highlight
 <span class="sd">            If set to ``True``, any previously-existing model with the same name will be reset (all checkpoints will</span>
 <span class="sd">            be discarded). Default: ``False``.</span>
 <span class="sd">        save_checkpoints</span>
-<span class="sd">            Whether or not to automatically save the untrained model and checkpoints from training.</span>
+<span class="sd">            Whether to automatically save the untrained model and checkpoints from training.</span>
 <span class="sd">            To load the model from checkpoint, call :func:`MyModelClass.load_from_checkpoint()`, where</span>
 <span class="sd">            :class:`MyModelClass` is the :class:`TorchForecastingModel` class that was used (such as :class:`TFTModel`,</span>
 <span class="sd">            :class:`NBEATSModel`, etc.). If set to ``False``, the model can still be manually saved using</span>
@@ -520,7 +582,6 @@ <h1>Source code for darts.models.forecasting.rnn_model</h1><div class="highlight
 <span class="sd">            &quot;devices&quot;, and &quot;auto_select_gpus&quot;``. Some examples for setting the devices inside the ``pl_trainer_kwargs``</span>
 <span class="sd">            dict:</span>
 
-
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;cpu&quot;}`` for CPU,</span>
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;gpu&quot;, &quot;devices&quot;: [i]}`` to use only GPU ``i`` (``i`` must be an integer),</span>
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;gpu&quot;, &quot;devices&quot;: -1, &quot;auto_select_gpus&quot;: True}`` to use all available GPUS.</span>
@@ -609,14 +670,14 @@ <h1>Source code for darts.models.forecasting.rnn_model</h1><div class="highlight
 
         <span class="c1"># check we got right model type specified:</span>
         <span class="k">if</span> <span class="n">model</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;RNN&quot;</span><span class="p">,</span> <span class="s2">&quot;LSTM&quot;</span><span class="p">,</span> <span class="s2">&quot;GRU&quot;</span><span class="p">]:</span>
-            <span class="n">raise_if_not</span><span class="p">(</span>
-                <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">),</span>
-                <span class="s1">&#39;</span><span class="si">{}</span><span class="s1"> is not a valid RNN model.</span><span class="se">\n</span><span class="s1"> Please specify &quot;RNN&quot;, &quot;LSTM&quot;, &#39;</span>
-                <span class="s1">&#39;&quot;GRU&quot;, or give your own PyTorch nn.Module&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                    <span class="n">model</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span>
-                <span class="p">),</span>
-                <span class="n">logger</span><span class="p">,</span>
-            <span class="p">)</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">inspect</span><span class="o">.</span><span class="n">isclass</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span> <span class="nb">issubclass</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">CustomRNNModule</span><span class="p">):</span>
+                <span class="n">raise_log</span><span class="p">(</span>
+                    <span class="ne">ValueError</span><span class="p">(</span>
+                        <span class="s2">&quot;`model` is not a valid RNN model. Please specify &#39;RNN&#39;, &#39;LSTM&#39;, &#39;GRU&#39;, or give a subclass &quot;</span>
+                        <span class="s2">&quot;(not an instance) of darts.models.forecasting.rnn_model.CustomRNNModule.&quot;</span>
+                    <span class="p">),</span>
+                    <span class="n">logger</span><span class="o">=</span><span class="n">logger</span><span class="p">,</span>
+                <span class="p">)</span>
 
         <span class="bp">self</span><span class="o">.</span><span class="n">rnn_type_or_module</span> <span class="o">=</span> <span class="n">model</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">dropout</span>
@@ -633,29 +694,22 @@ <h1>Source code for darts.models.forecasting.rnn_model</h1><div class="highlight
         <span class="n">output_dim</span> <span class="o">=</span> <span class="n">train_sample</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
         <span class="n">nr_params</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">likelihood</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">likelihood</span><span class="o">.</span><span class="n">num_parameters</span>
 
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">rnn_type_or_module</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;RNN&quot;</span><span class="p">,</span> <span class="s2">&quot;LSTM&quot;</span><span class="p">,</span> <span class="s2">&quot;GRU&quot;</span><span class="p">]:</span>
-            <span class="n">model</span> <span class="o">=</span> <span class="n">_RNNModule</span><span class="p">(</span>
-                <span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">rnn_type_or_module</span><span class="p">,</span>
-                <span class="n">input_size</span><span class="o">=</span><span class="n">input_dim</span><span class="p">,</span>
-                <span class="n">target_size</span><span class="o">=</span><span class="n">output_dim</span><span class="p">,</span>
-                <span class="n">nr_params</span><span class="o">=</span><span class="n">nr_params</span><span class="p">,</span>
-                <span class="n">hidden_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden_dim</span><span class="p">,</span>
-                <span class="n">dropout</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">,</span>
-                <span class="n">num_layers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_rnn_layers</span><span class="p">,</span>
-                <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">pl_module_params</span><span class="p">,</span>
-            <span class="p">)</span>
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rnn_type_or_module</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+            <span class="n">model_cls</span> <span class="o">=</span> <span class="n">_RNNModule</span>
+            <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rnn_type_or_module</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rnn_type_or_module</span><span class="p">(</span>
-                <span class="n">name</span><span class="o">=</span><span class="s2">&quot;custom_module&quot;</span><span class="p">,</span>
-                <span class="n">input_size</span><span class="o">=</span><span class="n">input_dim</span><span class="p">,</span>
-                <span class="n">target_size</span><span class="o">=</span><span class="n">output_dim</span><span class="p">,</span>
-                <span class="n">nr_params</span><span class="o">=</span><span class="n">nr_params</span><span class="p">,</span>
-                <span class="n">hidden_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden_dim</span><span class="p">,</span>
-                <span class="n">dropout</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">,</span>
-                <span class="n">num_layers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_rnn_layers</span><span class="p">,</span>
-                <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">pl_module_params</span><span class="p">,</span>
-            <span class="p">)</span>
-        <span class="k">return</span> <span class="n">model</span>
+            <span class="n">model_cls</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rnn_type_or_module</span>
+        <span class="k">return</span> <span class="n">model_cls</span><span class="p">(</span>
+            <span class="n">input_size</span><span class="o">=</span><span class="n">input_dim</span><span class="p">,</span>
+            <span class="n">target_size</span><span class="o">=</span><span class="n">output_dim</span><span class="p">,</span>
+            <span class="n">nr_params</span><span class="o">=</span><span class="n">nr_params</span><span class="p">,</span>
+            <span class="n">hidden_dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden_dim</span><span class="p">,</span>
+            <span class="n">dropout</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">,</span>
+            <span class="n">num_layers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_rnn_layers</span><span class="p">,</span>
+            <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">pl_module_params</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">)</span>
 
     <span class="k">def</span> <span class="nf">_build_train_dataset</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
diff --git a/_modules/darts/models/forecasting/sf_auto_ces.html b/_modules/darts/models/forecasting/sf_auto_ces.html
index 04f9c8ff65..c0d016b0bc 100644
--- a/_modules/darts/models/forecasting/sf_auto_ces.html
+++ b/_modules/darts/models/forecasting/sf_auto_ces.html
@@ -229,6 +229,7 @@ <h1>Source code for darts.models.forecasting.sf_auto_ces</h1><div class="highlig
         <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
         <span class="n">num_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
         <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">show_warnings</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
     <span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">)</span>
         <span class="n">forecast_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span>
diff --git a/_modules/darts/models/forecasting/sf_auto_theta.html b/_modules/darts/models/forecasting/sf_auto_theta.html
index c6563496b9..a5828d2684 100644
--- a/_modules/darts/models/forecasting/sf_auto_theta.html
+++ b/_modules/darts/models/forecasting/sf_auto_theta.html
@@ -237,6 +237,7 @@ <h1>Source code for darts.models.forecasting.sf_auto_theta</h1><div class="highl
         <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
         <span class="n">num_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
         <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">show_warnings</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
     <span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">)</span>
         <span class="n">forecast_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span>
diff --git a/_modules/darts/models/forecasting/tbats_model.html b/_modules/darts/models/forecasting/tbats_model.html
index 075ed79028..fce6868caf 100644
--- a/_modules/darts/models/forecasting/tbats_model.html
+++ b/_modules/darts/models/forecasting/tbats_model.html
@@ -397,7 +397,13 @@ <h1>Source code for darts.models.forecasting.tbats_model</h1><div class="highlig
 
         <span class="k">return</span> <span class="bp">self</span>
 
-    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">num_samples</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">num_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+        <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">show_warnings</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">)</span>
 
         <span class="n">yhat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">steps</span><span class="o">=</span><span class="n">n</span><span class="p">)</span>
diff --git a/_modules/darts/models/forecasting/tcn_model.html b/_modules/darts/models/forecasting/tcn_model.html
index ef48983596..2eb63f609a 100644
--- a/_modules/darts/models/forecasting/tcn_model.html
+++ b/_modules/darts/models/forecasting/tcn_model.html
@@ -521,7 +521,7 @@ <h1>Source code for darts.models.forecasting.tcn_model</h1><div class="highlight
 <span class="sd">            If set to ``True``, any previously-existing model with the same name will be reset (all checkpoints will</span>
 <span class="sd">            be discarded). Default: ``False``.</span>
 <span class="sd">        save_checkpoints</span>
-<span class="sd">            Whether or not to automatically save the untrained model and checkpoints from training.</span>
+<span class="sd">            Whether to automatically save the untrained model and checkpoints from training.</span>
 <span class="sd">            To load the model from checkpoint, call :func:`MyModelClass.load_from_checkpoint()`, where</span>
 <span class="sd">            :class:`MyModelClass` is the :class:`TorchForecastingModel` class that was used (such as :class:`TFTModel`,</span>
 <span class="sd">            :class:`NBEATSModel`, etc.). If set to ``False``, the model can still be manually saved using</span>
@@ -567,7 +567,6 @@ <h1>Source code for darts.models.forecasting.tcn_model</h1><div class="highlight
 <span class="sd">            dict:rgs``</span>
 <span class="sd">            dict:</span>
 
-
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;cpu&quot;}`` for CPU,</span>
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;gpu&quot;, &quot;devices&quot;: [i]}`` to use only GPU ``i`` (``i`` must be an integer),</span>
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;gpu&quot;, &quot;devices&quot;: -1, &quot;auto_select_gpus&quot;: True}`` to use all available GPUS.</span>
diff --git a/_modules/darts/models/forecasting/tft_model.html b/_modules/darts/models/forecasting/tft_model.html
index 3bd8684e6a..c271c9a467 100644
--- a/_modules/darts/models/forecasting/tft_model.html
+++ b/_modules/darts/models/forecasting/tft_model.html
@@ -971,7 +971,7 @@ <h1>Source code for darts.models.forecasting.tft_model</h1><div class="highlight
 <span class="sd">            If set to ``True``, any previously-existing model with the same name will be reset (all checkpoints will</span>
 <span class="sd">            be discarded). Default: ``False``.</span>
 <span class="sd">        save_checkpoints</span>
-<span class="sd">            Whether or not to automatically save the untrained model and checkpoints from training.</span>
+<span class="sd">            Whether to automatically save the untrained model and checkpoints from training.</span>
 <span class="sd">            To load the model from checkpoint, call :func:`MyModelClass.load_from_checkpoint()`, where</span>
 <span class="sd">            :class:`MyModelClass` is the :class:`TorchForecastingModel` class that was used (such as :class:`TFTModel`,</span>
 <span class="sd">            :class:`NBEATSModel`, etc.). If set to ``False``, the model can still be manually saved using</span>
@@ -1016,7 +1016,6 @@ <h1>Source code for darts.models.forecasting.tft_model</h1><div class="highlight
 <span class="sd">            &quot;devices&quot;, and &quot;auto_select_gpus&quot;``. Some examples for setting the devices inside the ``pl_trainer_kwargs``</span>
 <span class="sd">            dict:</span>
 
-
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;cpu&quot;}`` for CPU,</span>
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;gpu&quot;, &quot;devices&quot;: [i]}`` to use only GPU ``i`` (``i`` must be an integer),</span>
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;gpu&quot;, &quot;devices&quot;: -1, &quot;auto_select_gpus&quot;: True}`` to use all available GPUS.</span>
@@ -1357,17 +1356,7 @@ <h1>Source code for darts.models.forecasting.tft_model</h1><div class="highlight
 
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">supports_static_covariates</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-        <span class="k">return</span> <span class="kc">True</span>
-
-<div class="viewcode-block" id="TFTModel.predict"><a class="viewcode-back" href="../../../../generated_api/darts.models.forecasting.tft_model.html#darts.models.forecasting.tft_model.TFTModel.predict">[docs]</a>    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="c1"># since we have future covariates, the inference dataset for future input must be at least of length</span>
-        <span class="c1"># `output_chunk_length`. If not, we would have to step back which causes past input to be shorter than</span>
-        <span class="c1"># `input_chunk_length`.</span>
-
-        <span class="k">if</span> <span class="n">n</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_chunk_length</span><span class="p">:</span>
-            <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_chunk_length</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)[:</span><span class="n">n</span><span class="p">]</span></div></div>
+        <span class="k">return</span> <span class="kc">True</span></div>
 </pre></div>
 
               </div>
diff --git a/_modules/darts/models/forecasting/theta.html b/_modules/darts/models/forecasting/theta.html
index 00110ee9b9..8d07f8ce23 100644
--- a/_modules/darts/models/forecasting/theta.html
+++ b/_modules/darts/models/forecasting/theta.html
@@ -320,7 +320,11 @@ <h1>Source code for darts.models.forecasting.theta</h1><div class="highlight"><p
         <span class="k">return</span> <span class="bp">self</span></div>
 
 <div class="viewcode-block" id="Theta.predict"><a class="viewcode-back" href="../../../../generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.Theta.predict">[docs]</a>    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">num_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+        <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">show_warnings</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;TimeSeries&quot;</span><span class="p">:</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">)</span>
 
@@ -567,7 +571,11 @@ <h1>Source code for darts.models.forecasting.theta</h1><div class="highlight"><p
         <span class="k">return</span> <span class="bp">self</span></div>
 
 <div class="viewcode-block" id="FourTheta.predict"><a class="viewcode-back" href="../../../../generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.FourTheta.predict">[docs]</a>    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">num_samples</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+        <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">show_warnings</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;TimeSeries&quot;</span><span class="p">:</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">)</span>
 
diff --git a/_modules/darts/models/forecasting/tide_model.html b/_modules/darts/models/forecasting/tide_model.html
index e8535ff99a..ff0b713f96 100644
--- a/_modules/darts/models/forecasting/tide_model.html
+++ b/_modules/darts/models/forecasting/tide_model.html
@@ -645,7 +645,7 @@ <h1>Source code for darts.models.forecasting.tide_model</h1><div class="highligh
 <span class="sd">            If set to ``True``, any previously-existing model with the same name will be reset (all checkpoints will</span>
 <span class="sd">            be discarded). Default: ``False``.</span>
 <span class="sd">        save_checkpoints</span>
-<span class="sd">            Whether or not to automatically save the untrained model and checkpoints from training.</span>
+<span class="sd">            Whether to automatically save the untrained model and checkpoints from training.</span>
 <span class="sd">            To load the model from checkpoint, call :func:`MyModelClass.load_from_checkpoint()`, where</span>
 <span class="sd">            :class:`MyModelClass` is the :class:`TorchForecastingModel` class that was used (such as :class:`TFTModel`,</span>
 <span class="sd">            :class:`NBEATSModel`, etc.). If set to ``False``, the model can still be manually saved using</span>
@@ -690,7 +690,6 @@ <h1>Source code for darts.models.forecasting.tide_model</h1><div class="highligh
 <span class="sd">            &quot;devices&quot;, and &quot;auto_select_gpus&quot;``. Some examples for setting the devices inside the ``pl_trainer_kwargs``</span>
 <span class="sd">            dict:</span>
 
-
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;cpu&quot;}`` for CPU,</span>
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;gpu&quot;, &quot;devices&quot;: [i]}`` to use only GPU ``i`` (``i`` must be an integer),</span>
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;gpu&quot;, &quot;devices&quot;: -1, &quot;auto_select_gpus&quot;: True}`` to use all available GPUS.</span>
@@ -863,17 +862,7 @@ <h1>Source code for darts.models.forecasting.tide_model</h1><div class="highligh
 
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">supports_multivariate</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-        <span class="k">return</span> <span class="kc">True</span>
-
-<div class="viewcode-block" id="TiDEModel.predict"><a class="viewcode-back" href="../../../../generated_api/darts.models.forecasting.tide_model.html#darts.models.forecasting.tide_model.TiDEModel.predict">[docs]</a>    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="c1"># since we have future covariates, the inference dataset for future input must be at least of length</span>
-        <span class="c1"># `output_chunk_length`. If not, we would have to step back which causes past input to be shorter than</span>
-        <span class="c1"># `input_chunk_length`.</span>
-
-        <span class="k">if</span> <span class="n">n</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_chunk_length</span><span class="p">:</span>
-            <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_chunk_length</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)[:</span><span class="n">n</span><span class="p">]</span></div></div>
+        <span class="k">return</span> <span class="kc">True</span></div>
 </pre></div>
 
               </div>
diff --git a/_modules/darts/models/forecasting/transformer_model.html b/_modules/darts/models/forecasting/transformer_model.html
index 5a362907b5..541fe66a21 100644
--- a/_modules/darts/models/forecasting/transformer_model.html
+++ b/_modules/darts/models/forecasting/transformer_model.html
@@ -609,7 +609,7 @@ <h1>Source code for darts.models.forecasting.transformer_model</h1><div class="h
 <span class="sd">            If set to ``True``, any previously-existing model with the same name will be reset (all checkpoints will</span>
 <span class="sd">            be discarded). Default: ``False``.</span>
 <span class="sd">        save_checkpoints</span>
-<span class="sd">            Whether or not to automatically save the untrained model and checkpoints from training.</span>
+<span class="sd">            Whether to automatically save the untrained model and checkpoints from training.</span>
 <span class="sd">            To load the model from checkpoint, call :func:`MyModelClass.load_from_checkpoint()`, where</span>
 <span class="sd">            :class:`MyModelClass` is the :class:`TorchForecastingModel` class that was used (such as :class:`TFTModel`,</span>
 <span class="sd">            :class:`NBEATSModel`, etc.). If set to ``False``, the model can still be manually saved using</span>
@@ -654,7 +654,6 @@ <h1>Source code for darts.models.forecasting.transformer_model</h1><div class="h
 <span class="sd">            &quot;devices&quot;, and &quot;auto_select_gpus&quot;``. Some examples for setting the devices inside the ``pl_trainer_kwargs``</span>
 <span class="sd">            dict:</span>
 
-
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;cpu&quot;}`` for CPU,</span>
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;gpu&quot;, &quot;devices&quot;: [i]}`` to use only GPU ``i`` (``i`` must be an integer),</span>
 <span class="sd">            - ``{&quot;accelerator&quot;: &quot;gpu&quot;, &quot;devices&quot;: -1, &quot;auto_select_gpus&quot;: True}`` to use all available GPUS.</span>
diff --git a/_modules/darts/timeseries.html b/_modules/darts/timeseries.html
index 63fd083a55..1f506c05f7 100644
--- a/_modules/darts/timeseries.html
+++ b/_modules/darts/timeseries.html
@@ -1639,10 +1639,16 @@ <h1>Source code for darts.timeseries</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">_assert_deterministic</span><span class="p">()</span>
         <span class="k">if</span> <span class="n">copy</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_xa</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">index</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_time_index</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_xa</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span>
+                <span class="n">index</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_time_index</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span>
+                <span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">components</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
             <span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_xa</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_time_index</span><span class="p">)</span></div>
+            <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_xa</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+                <span class="n">index</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_time_index</span><span class="p">,</span>
+                <span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">components</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+            <span class="p">)</span></div>
 
 <div class="viewcode-block" id="TimeSeries.pd_dataframe"><a class="viewcode-back" href="../../generated_api/darts.timeseries.html#darts.timeseries.TimeSeries.pd_dataframe">[docs]</a>    <span class="k">def</span> <span class="nf">pd_dataframe</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">suppress_warnings</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
diff --git a/_modules/darts/utils/data/shifted_dataset.html b/_modules/darts/utils/data/shifted_dataset.html
index 25321be3cc..040d77f52b 100644
--- a/_modules/darts/utils/data/shifted_dataset.html
+++ b/_modules/darts/utils/data/shifted_dataset.html
@@ -688,7 +688,7 @@ <h1>Source code for darts.utils.data.shifted_dataset</h1><div class="highlight">
 <span class="sd">        shift</span>
 <span class="sd">            The number of time steps by which to shift the output chunks relative to the input chunks.</span>
 <span class="sd">        shift_covariates</span>
-<span class="sd">            Whether or not to shift the covariates forward the same way as the target.</span>
+<span class="sd">            Whether to shift the covariates forward the same way as the target.</span>
 <span class="sd">            FutureCovariatesModel&#39;s require this set to True, while PastCovariatesModel&#39;s require this set to False.</span>
 <span class="sd">        max_samples_per_ts</span>
 <span class="sd">            This is an upper bound on the number of (input, output, input_covariates) tuples that can be produced</span>
diff --git a/_sources/README.rst.txt b/_sources/README.rst.txt
index bb5876a2fc..e55638cbd6 100644
--- a/_sources/README.rst.txt
+++ b/_sources/README.rst.txt
@@ -30,7 +30,7 @@ Time Series Made Easy in Python
 
 
 .. image:: https://img.shields.io/docker/v/unit8/darts?label=docker&sort=date
-   :target: https://img.shields.io/docker/v/unit8/darts?label=docker&sort=date
+   :target: https://hub.docker.com/r/unit8/darts
    :alt: Docker Image Version (latest by date)
 
 
@@ -326,25 +326,25 @@ on bringing more models and features.
      - 
    * - `NaiveMean <https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMean>`_
      - 
-     - 🟩 🟥
+     - 🟩 🟩
      - 🟥 🟥 🟥
      - 🟥 🟥
      - 🟥
    * - `NaiveSeasonal <https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveSeasonal>`_
      - 
-     - 🟩 🟥
+     - 🟩 🟩
      - 🟥 🟥 🟥
      - 🟥 🟥
      - 🟥
    * - `NaiveDrift <https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveDrift>`_
      - 
-     - 🟩 🟥
+     - 🟩 🟩
      - 🟥 🟥 🟥
      - 🟥 🟥
      - 🟥
    * - `NaiveMovingAverage <https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMovingAverage>`_
      - 
-     - 🟩 🟥
+     - 🟩 🟩
      - 🟥 🟥 🟥
      - 🟥 🟥
      - 🟥
@@ -364,7 +364,7 @@ on bringing more models and features.
      - 
      - 🟥 🟩
      - 🟥 🟩 🟥
-     - 🟥 🟥
+     - 🟩 🟥
      - 🟥
    * - `AutoARIMA <https://unit8co.github.io/darts/generated_api/darts.models.forecasting.auto_arima.html#darts.models.forecasting.auto_arima.AutoARIMA>`_
      - 
@@ -414,7 +414,7 @@ on bringing more models and features.
      - 🟥 🟥 🟥
      - 🟩 🟥
      - 🟥
-   * - `Prophet <https://unit8co.github.io/darts/generated_api/darts.models.forecasting.prophet_model.html#darts.models.forecasting.prophet_model.Prophet>`_ (see `install notes <https://github.com/unit8co/darts/blob/master/INSTALL.md#enabling-support-for-facebook-prophet>`_\ )
+   * - `Prophet <https://unit8co.github.io/darts/generated_api/darts.models.forecasting.prophet_model.html#darts.models.forecasting.prophet_model.Prophet>`_
      - `Prophet repo <https://github.com/facebook/prophet>`_
      - 🟩 🟥
      - 🟥 🟩 🟥
@@ -462,7 +462,7 @@ on bringing more models and features.
      - 🟩 🟩 🟩
      - 🟥 🟥
      - 🟩
-   * - `LightGBMModel <https://unit8co.github.io/darts/generated_api/darts.models.forecasting.lgbm.html#darts.models.forecasting.lgbm.LightGBMModel>`_\ ,
+   * - `LightGBMModel <https://unit8co.github.io/darts/generated_api/darts.models.forecasting.lgbm.html#darts.models.forecasting.lgbm.LightGBMModel>`_
      - 
      - 🟩 🟩
      - 🟩 🟩 🟩
diff --git a/_sources/examples/01-multi-time-series-and-covariates.ipynb.txt b/_sources/examples/01-multi-time-series-and-covariates.ipynb.txt
index f3dea1d1bb..8744bb69a7 100644
--- a/_sources/examples/01-multi-time-series-and-covariates.ipynb.txt
+++ b/_sources/examples/01-multi-time-series-and-covariates.ipynb.txt
@@ -167,32 +167,31 @@
    "metadata": {},
    "source": [
     "## Global Forecasting Models\n",
-    "Darts contains many forecasting models, but not all of them can be trained on several time series. The models that support training on multiple series are called *global* models. At the time of writing, there are 5 global models:\n",
+    "Darts contains many forecasting models, but not all of them can be trained on several time series. The models that support training on multiple series are called *global* models. An exhaustive list of the global models can be found [here](https://unit8co.github.io/darts/README.html#forecasting-models) (bottom of the table) with for example:\n",
     "\n",
+    "* LinearRegressionModel\n",
     "* BlockRNNModel\n",
-    "* RNNModel\n",
-    "* Temporal Convolutional Networks (TCNs)\n",
-    "* N-Beats\n",
-    "* Transformer model\n",
+    "* Temporal Convolutional Networks (TCNModel)\n",
+    "* N-Beats (NBEATSModel)\n",
+    "* TiDEModel\n",
     "\n",
     "In the following, we will distinguish two sorts of time series:\n",
     "\n",
     "* The **target time series** is the time series we are interested to forecast (given its history)\n",
     "* A **covariate time series** is a time series which may help in the forecasting of the target series, but that we are not interested in forecasting. It's sometimes also called *external data*.\n",
     "\n",
+    "**Note**: Static covariates are invariant in time and correspond to additional information associated with the components of the **target time series**. Detailed information about this kind of covariate can be found in the [static covariates notebook](https://unit8co.github.io/darts/examples/15-static-covariates.html).\n",
+    "\n",
     "We further differentiate covariates series, depending on whether they can be known in advance or not:\n",
     "\n",
     "* **Past Covariates** denote time series whose past values are known at prediction time. These are usually things that have to be measured or observed.\n",
     "* **Future Covariates** denote time series whose future values are already known at prediction time for the span of the forecast horizon. These can for instance represent known future holidays, or weather forecasts. \n",
     "\n",
-    "Some models use only past covariates, others use only future covariates, and some models might use both. We will dive deeper in this topic in some other notebook, but for now it is enough to know this:\n",
-    "\n",
-    "* `BlockRNNModel`, `TCNModel`, `NBEATSModel` and `TransformerModel` all use `past_covariates`.\n",
-    "* `RNNModel` uses `future_covariates`.\n",
+    "Some models use only past covariates, others use only future covariates, and some models might use both. We will dive deeper in this topic in some other notebook, but this [table](https://unit8co.github.io/darts/README.html#forecasting-models) details the covariates supported by each model.\n",
     "\n",
-    "All of the global models listed above support training on multiple series. In addition, they also all support *multivariate series*. This means that they can seamlessly be used with time series of more than one dimension; the target series can contain one (as is often the case) or several dimensions. A time series with several dimensions is  really just a regular time series where the values at each time stamps are vectors instead of scalars.\n",
+    "All of the global models listed above support training on multiple series. In addition, they also all support *multivariate series*. This means that they can seamlessly be used with time series of more than one dimension; the target series can contain one (as is often the case) or several dimensions. A time series with several dimensions is really just a regular time series where the values at each time stamps are vectors instead of scalars.\n",
     "\n",
-    "As an example, the 4 models supporting `past_covariates` follow a \"block\" architecture. They contain a neural network that takes chunks of time series in input, and outputs chunks of (predicted) future time series values. The input dimensionality is the number of dimensions (components) of the target series, plus the number of components of all the covariates - stacked together. The output dimensionality is simply the number of dimensions of the target series:\n",
+    "As an example, the BlockRNNModel, N-Beats, TCN and Transformer models follow a \"block\" architecture. They contain a neural network that takes chunks of time series in input, and outputs chunks of (predicted) future time series values. The input dimensionality is the number of dimensions (components) of the target series, plus the number of components of all the covariates - stacked together. The output dimensionality is simply the number of dimensions of the target series:\n",
     "![](static/images/global_io_covs.png)\n",
     "\n",
     "The `RNNModel` works differently, in a recurrent fashion (which is also why they support future covariates).\n",
diff --git a/_sources/examples/20-RegressionModel-examples.ipynb.txt b/_sources/examples/20-RegressionModel-examples.ipynb.txt
index a63b2514de..30a84de818 100644
--- a/_sources/examples/20-RegressionModel-examples.ipynb.txt
+++ b/_sources/examples/20-RegressionModel-examples.ipynb.txt
@@ -502,7 +502,7 @@
     "This key parameter sets the *number of time steps that can be predicted at once by the internal regression model*.\n",
     "\n",
     "It is not the same as forecast horizon `n` from `predict()` which is the **desired** the number of generated prediction points, that is achieved either with:\n",
-    "- a single shot forecast (if `output_chunk_length <= n`), or\n",
+    "- a single shot forecast (if `n <= output_chunk_length`), or\n",
     "- an auto-regressive forecast, consuming its own forecast (and future values of covariates) as input for additional predictions (otherwise)\n",
     "\n",
     "For example, if we want our model to forecast the next 24 hours of electricity consumption based on the last day of consumption, setting `output_chunk_length=24` ensures that the model will not consume its forecasts or future values of our covariates to predict the entire day."
diff --git a/_sources/generated_api/darts.ad.aggregators.aggregators.rst.txt b/_sources/generated_api/darts.ad.aggregators.aggregators.rst.txt
index 96c1d96410..864d4ae2aa 100644
--- a/_sources/generated_api/darts.ad.aggregators.aggregators.rst.txt
+++ b/_sources/generated_api/darts.ad.aggregators.aggregators.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.aggregators.aggregators
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.aggregators.and_aggregator.rst.txt b/_sources/generated_api/darts.ad.aggregators.and_aggregator.rst.txt
index bb161f0ce8..b2a45d4a50 100644
--- a/_sources/generated_api/darts.ad.aggregators.and_aggregator.rst.txt
+++ b/_sources/generated_api/darts.ad.aggregators.and_aggregator.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.aggregators.and_aggregator
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.aggregators.ensemble_sklearn_aggregator.rst.txt b/_sources/generated_api/darts.ad.aggregators.ensemble_sklearn_aggregator.rst.txt
index dd95b17b96..ad69483bdb 100644
--- a/_sources/generated_api/darts.ad.aggregators.ensemble_sklearn_aggregator.rst.txt
+++ b/_sources/generated_api/darts.ad.aggregators.ensemble_sklearn_aggregator.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.aggregators.ensemble_sklearn_aggregator
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.aggregators.or_aggregator.rst.txt b/_sources/generated_api/darts.ad.aggregators.or_aggregator.rst.txt
index 261df99928..ebdc784138 100644
--- a/_sources/generated_api/darts.ad.aggregators.or_aggregator.rst.txt
+++ b/_sources/generated_api/darts.ad.aggregators.or_aggregator.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.aggregators.or_aggregator
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.anomaly_model.anomaly_model.rst.txt b/_sources/generated_api/darts.ad.anomaly_model.anomaly_model.rst.txt
index 5fd4e54b7a..7c3e739cab 100644
--- a/_sources/generated_api/darts.ad.anomaly_model.anomaly_model.rst.txt
+++ b/_sources/generated_api/darts.ad.anomaly_model.anomaly_model.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.anomaly_model.anomaly_model
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.anomaly_model.filtering_am.rst.txt b/_sources/generated_api/darts.ad.anomaly_model.filtering_am.rst.txt
index 3bdffe9fe4..a37fa89c67 100644
--- a/_sources/generated_api/darts.ad.anomaly_model.filtering_am.rst.txt
+++ b/_sources/generated_api/darts.ad.anomaly_model.filtering_am.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.anomaly_model.filtering_am
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.anomaly_model.forecasting_am.rst.txt b/_sources/generated_api/darts.ad.anomaly_model.forecasting_am.rst.txt
index be7d5dce02..7a0f44e83e 100644
--- a/_sources/generated_api/darts.ad.anomaly_model.forecasting_am.rst.txt
+++ b/_sources/generated_api/darts.ad.anomaly_model.forecasting_am.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.anomaly_model.forecasting_am
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.detectors.detectors.rst.txt b/_sources/generated_api/darts.ad.detectors.detectors.rst.txt
index b0fa330d00..9895c1d298 100644
--- a/_sources/generated_api/darts.ad.detectors.detectors.rst.txt
+++ b/_sources/generated_api/darts.ad.detectors.detectors.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.detectors.detectors
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.detectors.quantile_detector.rst.txt b/_sources/generated_api/darts.ad.detectors.quantile_detector.rst.txt
index 6c1eac1493..914b931f46 100644
--- a/_sources/generated_api/darts.ad.detectors.quantile_detector.rst.txt
+++ b/_sources/generated_api/darts.ad.detectors.quantile_detector.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.detectors.quantile_detector
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.detectors.threshold_detector.rst.txt b/_sources/generated_api/darts.ad.detectors.threshold_detector.rst.txt
index 1797d76870..0e5e18b94e 100644
--- a/_sources/generated_api/darts.ad.detectors.threshold_detector.rst.txt
+++ b/_sources/generated_api/darts.ad.detectors.threshold_detector.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.detectors.threshold_detector
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.scorers.difference_scorer.rst.txt b/_sources/generated_api/darts.ad.scorers.difference_scorer.rst.txt
index 4047248c1d..a4303a7956 100644
--- a/_sources/generated_api/darts.ad.scorers.difference_scorer.rst.txt
+++ b/_sources/generated_api/darts.ad.scorers.difference_scorer.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.scorers.difference_scorer
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.scorers.kmeans_scorer.rst.txt b/_sources/generated_api/darts.ad.scorers.kmeans_scorer.rst.txt
index 883e81b471..cdb9645817 100644
--- a/_sources/generated_api/darts.ad.scorers.kmeans_scorer.rst.txt
+++ b/_sources/generated_api/darts.ad.scorers.kmeans_scorer.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.scorers.kmeans_scorer
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.scorers.nll_cauchy_scorer.rst.txt b/_sources/generated_api/darts.ad.scorers.nll_cauchy_scorer.rst.txt
index 917a37a44a..b05d5fb15d 100644
--- a/_sources/generated_api/darts.ad.scorers.nll_cauchy_scorer.rst.txt
+++ b/_sources/generated_api/darts.ad.scorers.nll_cauchy_scorer.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.scorers.nll_cauchy_scorer
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.scorers.nll_exponential_scorer.rst.txt b/_sources/generated_api/darts.ad.scorers.nll_exponential_scorer.rst.txt
index da0b1ae74a..cb2e15d3f3 100644
--- a/_sources/generated_api/darts.ad.scorers.nll_exponential_scorer.rst.txt
+++ b/_sources/generated_api/darts.ad.scorers.nll_exponential_scorer.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.scorers.nll_exponential_scorer
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.scorers.nll_gamma_scorer.rst.txt b/_sources/generated_api/darts.ad.scorers.nll_gamma_scorer.rst.txt
index ed86017478..8b6370edc0 100644
--- a/_sources/generated_api/darts.ad.scorers.nll_gamma_scorer.rst.txt
+++ b/_sources/generated_api/darts.ad.scorers.nll_gamma_scorer.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.scorers.nll_gamma_scorer
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.scorers.nll_gaussian_scorer.rst.txt b/_sources/generated_api/darts.ad.scorers.nll_gaussian_scorer.rst.txt
index 08d076a0c9..8b190386a0 100644
--- a/_sources/generated_api/darts.ad.scorers.nll_gaussian_scorer.rst.txt
+++ b/_sources/generated_api/darts.ad.scorers.nll_gaussian_scorer.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.scorers.nll_gaussian_scorer
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.scorers.nll_laplace_scorer.rst.txt b/_sources/generated_api/darts.ad.scorers.nll_laplace_scorer.rst.txt
index 8694c1ac6f..b143b5f57e 100644
--- a/_sources/generated_api/darts.ad.scorers.nll_laplace_scorer.rst.txt
+++ b/_sources/generated_api/darts.ad.scorers.nll_laplace_scorer.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.scorers.nll_laplace_scorer
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.scorers.nll_poisson_scorer.rst.txt b/_sources/generated_api/darts.ad.scorers.nll_poisson_scorer.rst.txt
index 070fa465e7..7a37a4a415 100644
--- a/_sources/generated_api/darts.ad.scorers.nll_poisson_scorer.rst.txt
+++ b/_sources/generated_api/darts.ad.scorers.nll_poisson_scorer.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.scorers.nll_poisson_scorer
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.scorers.norm_scorer.rst.txt b/_sources/generated_api/darts.ad.scorers.norm_scorer.rst.txt
index 5535c52dc6..ecd438208f 100644
--- a/_sources/generated_api/darts.ad.scorers.norm_scorer.rst.txt
+++ b/_sources/generated_api/darts.ad.scorers.norm_scorer.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.scorers.norm_scorer
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.scorers.pyod_scorer.rst.txt b/_sources/generated_api/darts.ad.scorers.pyod_scorer.rst.txt
index 4ba5fd4cd9..06cf2ea9c8 100644
--- a/_sources/generated_api/darts.ad.scorers.pyod_scorer.rst.txt
+++ b/_sources/generated_api/darts.ad.scorers.pyod_scorer.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.scorers.pyod_scorer
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.scorers.scorers.rst.txt b/_sources/generated_api/darts.ad.scorers.scorers.rst.txt
index 7f2ba5852f..61eed91745 100644
--- a/_sources/generated_api/darts.ad.scorers.scorers.rst.txt
+++ b/_sources/generated_api/darts.ad.scorers.scorers.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.scorers.scorers
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.scorers.wasserstein_scorer.rst.txt b/_sources/generated_api/darts.ad.scorers.wasserstein_scorer.rst.txt
index d8ce8be436..7bfdfde63c 100644
--- a/_sources/generated_api/darts.ad.scorers.wasserstein_scorer.rst.txt
+++ b/_sources/generated_api/darts.ad.scorers.wasserstein_scorer.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.scorers.wasserstein_scorer
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.ad.utils.rst.txt b/_sources/generated_api/darts.ad.utils.rst.txt
index c89dcd593b..350988ccf2 100644
--- a/_sources/generated_api/darts.ad.utils.rst.txt
+++ b/_sources/generated_api/darts.ad.utils.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.ad.utils
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.dataprocessing.dtw.cost_matrix.rst.txt b/_sources/generated_api/darts.dataprocessing.dtw.cost_matrix.rst.txt
index 5add4afe18..4eb42b4d02 100644
--- a/_sources/generated_api/darts.dataprocessing.dtw.cost_matrix.rst.txt
+++ b/_sources/generated_api/darts.dataprocessing.dtw.cost_matrix.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.dataprocessing.dtw.cost_matrix
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.dataprocessing.dtw.dtw.rst.txt b/_sources/generated_api/darts.dataprocessing.dtw.dtw.rst.txt
index aef4df5fe3..242e418a1f 100644
--- a/_sources/generated_api/darts.dataprocessing.dtw.dtw.rst.txt
+++ b/_sources/generated_api/darts.dataprocessing.dtw.dtw.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.dataprocessing.dtw.dtw
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.dataprocessing.dtw.window.rst.txt b/_sources/generated_api/darts.dataprocessing.dtw.window.rst.txt
index 382e61cffc..1b4f71ac9a 100644
--- a/_sources/generated_api/darts.dataprocessing.dtw.window.rst.txt
+++ b/_sources/generated_api/darts.dataprocessing.dtw.window.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.dataprocessing.dtw.window
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.dataprocessing.encoders.encoder_base.rst.txt b/_sources/generated_api/darts.dataprocessing.encoders.encoder_base.rst.txt
index dadc0d52b5..2cb8c21894 100644
--- a/_sources/generated_api/darts.dataprocessing.encoders.encoder_base.rst.txt
+++ b/_sources/generated_api/darts.dataprocessing.encoders.encoder_base.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.dataprocessing.encoders.encoder_base
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.dataprocessing.encoders.encoders.rst.txt b/_sources/generated_api/darts.dataprocessing.encoders.encoders.rst.txt
index 4d520abb32..46d5168e9c 100644
--- a/_sources/generated_api/darts.dataprocessing.encoders.encoders.rst.txt
+++ b/_sources/generated_api/darts.dataprocessing.encoders.encoders.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.dataprocessing.encoders.encoders
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.dataprocessing.pipeline.rst.txt b/_sources/generated_api/darts.dataprocessing.pipeline.rst.txt
index 43c3c607aa..2c269b8808 100644
--- a/_sources/generated_api/darts.dataprocessing.pipeline.rst.txt
+++ b/_sources/generated_api/darts.dataprocessing.pipeline.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.dataprocessing.pipeline
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.dataprocessing.transformers.base_data_transformer.rst.txt b/_sources/generated_api/darts.dataprocessing.transformers.base_data_transformer.rst.txt
index 3db3758a30..80f619d599 100644
--- a/_sources/generated_api/darts.dataprocessing.transformers.base_data_transformer.rst.txt
+++ b/_sources/generated_api/darts.dataprocessing.transformers.base_data_transformer.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.dataprocessing.transformers.base_data_transformer
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.dataprocessing.transformers.boxcox.rst.txt b/_sources/generated_api/darts.dataprocessing.transformers.boxcox.rst.txt
index 982c2d4cf4..87a18f33bd 100644
--- a/_sources/generated_api/darts.dataprocessing.transformers.boxcox.rst.txt
+++ b/_sources/generated_api/darts.dataprocessing.transformers.boxcox.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.dataprocessing.transformers.boxcox
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.dataprocessing.transformers.diff.rst.txt b/_sources/generated_api/darts.dataprocessing.transformers.diff.rst.txt
index 41c1a5f4b1..635cdfa12d 100644
--- a/_sources/generated_api/darts.dataprocessing.transformers.diff.rst.txt
+++ b/_sources/generated_api/darts.dataprocessing.transformers.diff.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.dataprocessing.transformers.diff
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.dataprocessing.transformers.fittable_data_transformer.rst.txt b/_sources/generated_api/darts.dataprocessing.transformers.fittable_data_transformer.rst.txt
index 29d578f412..fb94d084f5 100644
--- a/_sources/generated_api/darts.dataprocessing.transformers.fittable_data_transformer.rst.txt
+++ b/_sources/generated_api/darts.dataprocessing.transformers.fittable_data_transformer.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.dataprocessing.transformers.fittable_data_transformer
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.dataprocessing.transformers.invertible_data_transformer.rst.txt b/_sources/generated_api/darts.dataprocessing.transformers.invertible_data_transformer.rst.txt
index 689319d4c0..f233e0dc73 100644
--- a/_sources/generated_api/darts.dataprocessing.transformers.invertible_data_transformer.rst.txt
+++ b/_sources/generated_api/darts.dataprocessing.transformers.invertible_data_transformer.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.dataprocessing.transformers.invertible_data_transformer
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.dataprocessing.transformers.mappers.rst.txt b/_sources/generated_api/darts.dataprocessing.transformers.mappers.rst.txt
index 382b40f7bd..74f53b3055 100644
--- a/_sources/generated_api/darts.dataprocessing.transformers.mappers.rst.txt
+++ b/_sources/generated_api/darts.dataprocessing.transformers.mappers.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.dataprocessing.transformers.mappers
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.dataprocessing.transformers.midas.rst.txt b/_sources/generated_api/darts.dataprocessing.transformers.midas.rst.txt
index f44f8cd05f..810ab9c089 100644
--- a/_sources/generated_api/darts.dataprocessing.transformers.midas.rst.txt
+++ b/_sources/generated_api/darts.dataprocessing.transformers.midas.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.dataprocessing.transformers.midas
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.dataprocessing.transformers.missing_values_filler.rst.txt b/_sources/generated_api/darts.dataprocessing.transformers.missing_values_filler.rst.txt
index ddd534d307..d34e8e7b19 100644
--- a/_sources/generated_api/darts.dataprocessing.transformers.missing_values_filler.rst.txt
+++ b/_sources/generated_api/darts.dataprocessing.transformers.missing_values_filler.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.dataprocessing.transformers.missing_values_filler
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.dataprocessing.transformers.reconciliation.rst.txt b/_sources/generated_api/darts.dataprocessing.transformers.reconciliation.rst.txt
index 109b2f68d9..9f5d584e56 100644
--- a/_sources/generated_api/darts.dataprocessing.transformers.reconciliation.rst.txt
+++ b/_sources/generated_api/darts.dataprocessing.transformers.reconciliation.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.dataprocessing.transformers.reconciliation
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.dataprocessing.transformers.scaler.rst.txt b/_sources/generated_api/darts.dataprocessing.transformers.scaler.rst.txt
index 53d53aede4..faca7cacf5 100644
--- a/_sources/generated_api/darts.dataprocessing.transformers.scaler.rst.txt
+++ b/_sources/generated_api/darts.dataprocessing.transformers.scaler.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.dataprocessing.transformers.scaler
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.dataprocessing.transformers.static_covariates_transformer.rst.txt b/_sources/generated_api/darts.dataprocessing.transformers.static_covariates_transformer.rst.txt
index 3adab261f3..36521c354c 100644
--- a/_sources/generated_api/darts.dataprocessing.transformers.static_covariates_transformer.rst.txt
+++ b/_sources/generated_api/darts.dataprocessing.transformers.static_covariates_transformer.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.dataprocessing.transformers.static_covariates_transformer
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.dataprocessing.transformers.window_transformer.rst.txt b/_sources/generated_api/darts.dataprocessing.transformers.window_transformer.rst.txt
index 4ff316e87f..202c456c0e 100644
--- a/_sources/generated_api/darts.dataprocessing.transformers.window_transformer.rst.txt
+++ b/_sources/generated_api/darts.dataprocessing.transformers.window_transformer.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.dataprocessing.transformers.window_transformer
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.datasets.dataset_loaders.rst.txt b/_sources/generated_api/darts.datasets.dataset_loaders.rst.txt
index ffb9e72526..17d9225f46 100644
--- a/_sources/generated_api/darts.datasets.dataset_loaders.rst.txt
+++ b/_sources/generated_api/darts.datasets.dataset_loaders.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.datasets.dataset_loaders
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.explainability.explainability.rst.txt b/_sources/generated_api/darts.explainability.explainability.rst.txt
index fccc9f4c77..5f0747864a 100644
--- a/_sources/generated_api/darts.explainability.explainability.rst.txt
+++ b/_sources/generated_api/darts.explainability.explainability.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.explainability.explainability
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.explainability.explainability_result.rst.txt b/_sources/generated_api/darts.explainability.explainability_result.rst.txt
index d87f374c42..d2791b7a71 100644
--- a/_sources/generated_api/darts.explainability.explainability_result.rst.txt
+++ b/_sources/generated_api/darts.explainability.explainability_result.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.explainability.explainability_result
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.explainability.shap_explainer.rst.txt b/_sources/generated_api/darts.explainability.shap_explainer.rst.txt
index 7abca313b4..9f59ad7d2e 100644
--- a/_sources/generated_api/darts.explainability.shap_explainer.rst.txt
+++ b/_sources/generated_api/darts.explainability.shap_explainer.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.explainability.shap_explainer
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.explainability.tft_explainer.rst.txt b/_sources/generated_api/darts.explainability.tft_explainer.rst.txt
index 1ca95ef09a..6e1afeac3a 100644
--- a/_sources/generated_api/darts.explainability.tft_explainer.rst.txt
+++ b/_sources/generated_api/darts.explainability.tft_explainer.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.explainability.tft_explainer
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.explainability.utils.rst.txt b/_sources/generated_api/darts.explainability.utils.rst.txt
index c468aac381..6cf9ae72be 100644
--- a/_sources/generated_api/darts.explainability.utils.rst.txt
+++ b/_sources/generated_api/darts.explainability.utils.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.explainability.utils
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.metrics.metrics.rst.txt b/_sources/generated_api/darts.metrics.metrics.rst.txt
index 559e44894a..c097d0dd97 100644
--- a/_sources/generated_api/darts.metrics.metrics.rst.txt
+++ b/_sources/generated_api/darts.metrics.metrics.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.metrics.metrics
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.components.feed_forward.rst.txt b/_sources/generated_api/darts.models.components.feed_forward.rst.txt
index 9a1c272341..06a330efd7 100644
--- a/_sources/generated_api/darts.models.components.feed_forward.rst.txt
+++ b/_sources/generated_api/darts.models.components.feed_forward.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.components.feed_forward
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.components.glu_variants.rst.txt b/_sources/generated_api/darts.models.components.glu_variants.rst.txt
index 34fe8e99ec..c01c772d5e 100644
--- a/_sources/generated_api/darts.models.components.glu_variants.rst.txt
+++ b/_sources/generated_api/darts.models.components.glu_variants.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.components.glu_variants
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.components.layer_norm_variants.rst.txt b/_sources/generated_api/darts.models.components.layer_norm_variants.rst.txt
index a8af8dcb2b..0c8b83e9d9 100644
--- a/_sources/generated_api/darts.models.components.layer_norm_variants.rst.txt
+++ b/_sources/generated_api/darts.models.components.layer_norm_variants.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.components.layer_norm_variants
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.components.statsforecast_utils.rst.txt b/_sources/generated_api/darts.models.components.statsforecast_utils.rst.txt
index b7fc4b0b43..6a5526bab7 100644
--- a/_sources/generated_api/darts.models.components.statsforecast_utils.rst.txt
+++ b/_sources/generated_api/darts.models.components.statsforecast_utils.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.components.statsforecast_utils
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.components.transformer.rst.txt b/_sources/generated_api/darts.models.components.transformer.rst.txt
index 45d0531d67..075cccf9c8 100644
--- a/_sources/generated_api/darts.models.components.transformer.rst.txt
+++ b/_sources/generated_api/darts.models.components.transformer.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.components.transformer
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.filtering.filtering_model.rst.txt b/_sources/generated_api/darts.models.filtering.filtering_model.rst.txt
index 2dca0c83e4..450545a5df 100644
--- a/_sources/generated_api/darts.models.filtering.filtering_model.rst.txt
+++ b/_sources/generated_api/darts.models.filtering.filtering_model.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.filtering.filtering_model
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.filtering.gaussian_process_filter.rst.txt b/_sources/generated_api/darts.models.filtering.gaussian_process_filter.rst.txt
index ec2754f886..51f89eac02 100644
--- a/_sources/generated_api/darts.models.filtering.gaussian_process_filter.rst.txt
+++ b/_sources/generated_api/darts.models.filtering.gaussian_process_filter.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.filtering.gaussian_process_filter
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.filtering.kalman_filter.rst.txt b/_sources/generated_api/darts.models.filtering.kalman_filter.rst.txt
index 78e009b38a..a1428721d5 100644
--- a/_sources/generated_api/darts.models.filtering.kalman_filter.rst.txt
+++ b/_sources/generated_api/darts.models.filtering.kalman_filter.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.filtering.kalman_filter
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.filtering.moving_average_filter.rst.txt b/_sources/generated_api/darts.models.filtering.moving_average_filter.rst.txt
index 8557c6d6a3..9b4670ece1 100644
--- a/_sources/generated_api/darts.models.filtering.moving_average_filter.rst.txt
+++ b/_sources/generated_api/darts.models.filtering.moving_average_filter.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.filtering.moving_average_filter
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.arima.rst.txt b/_sources/generated_api/darts.models.forecasting.arima.rst.txt
index f5f3ba19db..559c2de2bd 100644
--- a/_sources/generated_api/darts.models.forecasting.arima.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.arima.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.arima
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.auto_arima.rst.txt b/_sources/generated_api/darts.models.forecasting.auto_arima.rst.txt
index f221cc8333..182332fbd7 100644
--- a/_sources/generated_api/darts.models.forecasting.auto_arima.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.auto_arima.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.auto_arima
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.baselines.rst.txt b/_sources/generated_api/darts.models.forecasting.baselines.rst.txt
index a5606cd207..5091d44b85 100644
--- a/_sources/generated_api/darts.models.forecasting.baselines.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.baselines.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.baselines
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.block_rnn_model.rst.txt b/_sources/generated_api/darts.models.forecasting.block_rnn_model.rst.txt
index 508509c2b6..149cc6f3ae 100644
--- a/_sources/generated_api/darts.models.forecasting.block_rnn_model.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.block_rnn_model.rst.txt
@@ -1,4 +1,10 @@
+Block Recurrent Neural Networks
+-------------------------------
 
-.. automodule:: darts.models.forecasting.block_rnn_model
+.. autoclass:: darts.models.forecasting.block_rnn_model.BlockRNNModel
    :members:
-   :undoc-members:
\ No newline at end of file
+   :exclude-members:
+
+.. autoclass:: darts.models.forecasting.block_rnn_model.CustomBlockRNNModule
+   :members:
+   :no-inherited-members:
diff --git a/_sources/generated_api/darts.models.forecasting.catboost_model.rst.txt b/_sources/generated_api/darts.models.forecasting.catboost_model.rst.txt
index 4d5433ba1b..1b77e0178c 100644
--- a/_sources/generated_api/darts.models.forecasting.catboost_model.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.catboost_model.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.catboost_model
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.croston.rst.txt b/_sources/generated_api/darts.models.forecasting.croston.rst.txt
index 111730ffc8..989be8c208 100644
--- a/_sources/generated_api/darts.models.forecasting.croston.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.croston.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.croston
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.dlinear.rst.txt b/_sources/generated_api/darts.models.forecasting.dlinear.rst.txt
index d49cf3ee2a..50edffdd72 100644
--- a/_sources/generated_api/darts.models.forecasting.dlinear.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.dlinear.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.dlinear
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.ensemble_model.rst.txt b/_sources/generated_api/darts.models.forecasting.ensemble_model.rst.txt
index 344a91bc57..7a5acf9bb3 100644
--- a/_sources/generated_api/darts.models.forecasting.ensemble_model.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.ensemble_model.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.ensemble_model
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.exponential_smoothing.rst.txt b/_sources/generated_api/darts.models.forecasting.exponential_smoothing.rst.txt
index 14a251ca66..6394175456 100644
--- a/_sources/generated_api/darts.models.forecasting.exponential_smoothing.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.exponential_smoothing.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.exponential_smoothing
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.fft.rst.txt b/_sources/generated_api/darts.models.forecasting.fft.rst.txt
index a0e497fd08..b597f74697 100644
--- a/_sources/generated_api/darts.models.forecasting.fft.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.fft.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.fft
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.forecasting_model.rst.txt b/_sources/generated_api/darts.models.forecasting.forecasting_model.rst.txt
index 87fa515b55..0d1ca08145 100644
--- a/_sources/generated_api/darts.models.forecasting.forecasting_model.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.forecasting_model.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.forecasting_model
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.kalman_forecaster.rst.txt b/_sources/generated_api/darts.models.forecasting.kalman_forecaster.rst.txt
index ebdf697982..1d0c10c497 100644
--- a/_sources/generated_api/darts.models.forecasting.kalman_forecaster.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.kalman_forecaster.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.kalman_forecaster
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.lgbm.rst.txt b/_sources/generated_api/darts.models.forecasting.lgbm.rst.txt
index d82ff3c39e..c6b039e6d5 100644
--- a/_sources/generated_api/darts.models.forecasting.lgbm.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.lgbm.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.lgbm
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.linear_regression_model.rst.txt b/_sources/generated_api/darts.models.forecasting.linear_regression_model.rst.txt
index 6a706f7789..90a2893437 100644
--- a/_sources/generated_api/darts.models.forecasting.linear_regression_model.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.linear_regression_model.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.linear_regression_model
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.nbeats.rst.txt b/_sources/generated_api/darts.models.forecasting.nbeats.rst.txt
index 35d426552b..9226d32a17 100644
--- a/_sources/generated_api/darts.models.forecasting.nbeats.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.nbeats.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.nbeats
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.nhits.rst.txt b/_sources/generated_api/darts.models.forecasting.nhits.rst.txt
index 589d5fbb59..7fdd3a3b11 100644
--- a/_sources/generated_api/darts.models.forecasting.nhits.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.nhits.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.nhits
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.nlinear.rst.txt b/_sources/generated_api/darts.models.forecasting.nlinear.rst.txt
index 90e70fdb1d..6e0e29d010 100644
--- a/_sources/generated_api/darts.models.forecasting.nlinear.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.nlinear.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.nlinear
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.pl_forecasting_module.rst.txt b/_sources/generated_api/darts.models.forecasting.pl_forecasting_module.rst.txt
index 516742c993..0c0a2b877d 100644
--- a/_sources/generated_api/darts.models.forecasting.pl_forecasting_module.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.pl_forecasting_module.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.pl_forecasting_module
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.prophet_model.rst.txt b/_sources/generated_api/darts.models.forecasting.prophet_model.rst.txt
index 0f8cadb234..d4540abf43 100644
--- a/_sources/generated_api/darts.models.forecasting.prophet_model.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.prophet_model.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.prophet_model
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.random_forest.rst.txt b/_sources/generated_api/darts.models.forecasting.random_forest.rst.txt
index 139d5aa9c0..6e5c18bedf 100644
--- a/_sources/generated_api/darts.models.forecasting.random_forest.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.random_forest.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.random_forest
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.regression_ensemble_model.rst.txt b/_sources/generated_api/darts.models.forecasting.regression_ensemble_model.rst.txt
index 0590a53de1..2e2838b486 100644
--- a/_sources/generated_api/darts.models.forecasting.regression_ensemble_model.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.regression_ensemble_model.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.regression_ensemble_model
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.regression_model.rst.txt b/_sources/generated_api/darts.models.forecasting.regression_model.rst.txt
index 10b8e4ef69..56ed09b5a5 100644
--- a/_sources/generated_api/darts.models.forecasting.regression_model.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.regression_model.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.regression_model
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.rnn_model.rst.txt b/_sources/generated_api/darts.models.forecasting.rnn_model.rst.txt
index 11c903bccc..31ecf93cbd 100644
--- a/_sources/generated_api/darts.models.forecasting.rnn_model.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.rnn_model.rst.txt
@@ -1,4 +1,10 @@
+Recurrent Neural Networks
+-------------------------
 
-.. automodule:: darts.models.forecasting.rnn_model
+.. autoclass:: darts.models.forecasting.rnn_model.RNNModel
    :members:
-   :undoc-members:
\ No newline at end of file
+   :exclude-members:
+
+.. autoclass:: darts.models.forecasting.rnn_model.CustomRNNModule
+   :members:
+   :no-inherited-members:
diff --git a/_sources/generated_api/darts.models.forecasting.sf_auto_arima.rst.txt b/_sources/generated_api/darts.models.forecasting.sf_auto_arima.rst.txt
index e50a33b2c1..76418c50e4 100644
--- a/_sources/generated_api/darts.models.forecasting.sf_auto_arima.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.sf_auto_arima.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.sf_auto_arima
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.sf_auto_ces.rst.txt b/_sources/generated_api/darts.models.forecasting.sf_auto_ces.rst.txt
index 97ec5623fd..7091d50b74 100644
--- a/_sources/generated_api/darts.models.forecasting.sf_auto_ces.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.sf_auto_ces.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.sf_auto_ces
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.sf_auto_ets.rst.txt b/_sources/generated_api/darts.models.forecasting.sf_auto_ets.rst.txt
index a789d8edfd..2eb3546348 100644
--- a/_sources/generated_api/darts.models.forecasting.sf_auto_ets.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.sf_auto_ets.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.sf_auto_ets
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.sf_auto_theta.rst.txt b/_sources/generated_api/darts.models.forecasting.sf_auto_theta.rst.txt
index 33bff03eec..8eebde970c 100644
--- a/_sources/generated_api/darts.models.forecasting.sf_auto_theta.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.sf_auto_theta.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.sf_auto_theta
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.tbats_model.rst.txt b/_sources/generated_api/darts.models.forecasting.tbats_model.rst.txt
index d8dc517ffd..e6d1932a0e 100644
--- a/_sources/generated_api/darts.models.forecasting.tbats_model.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.tbats_model.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.tbats_model
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.tcn_model.rst.txt b/_sources/generated_api/darts.models.forecasting.tcn_model.rst.txt
index 6306157ced..a85fcc7923 100644
--- a/_sources/generated_api/darts.models.forecasting.tcn_model.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.tcn_model.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.tcn_model
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.tft_model.rst.txt b/_sources/generated_api/darts.models.forecasting.tft_model.rst.txt
index 30312491d2..ae6587e9bb 100644
--- a/_sources/generated_api/darts.models.forecasting.tft_model.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.tft_model.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.tft_model
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.tft_submodels.rst.txt b/_sources/generated_api/darts.models.forecasting.tft_submodels.rst.txt
index db8678b1ce..58a435f161 100644
--- a/_sources/generated_api/darts.models.forecasting.tft_submodels.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.tft_submodels.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.tft_submodels
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.theta.rst.txt b/_sources/generated_api/darts.models.forecasting.theta.rst.txt
index fc9ec96bac..735b8d41cc 100644
--- a/_sources/generated_api/darts.models.forecasting.theta.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.theta.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.theta
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.tide_model.rst.txt b/_sources/generated_api/darts.models.forecasting.tide_model.rst.txt
index 1be8511824..d8081d9f20 100644
--- a/_sources/generated_api/darts.models.forecasting.tide_model.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.tide_model.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.tide_model
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.torch_forecasting_model.rst.txt b/_sources/generated_api/darts.models.forecasting.torch_forecasting_model.rst.txt
index ee2eb9a100..60324c59f3 100644
--- a/_sources/generated_api/darts.models.forecasting.torch_forecasting_model.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.torch_forecasting_model.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.torch_forecasting_model
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.transformer_model.rst.txt b/_sources/generated_api/darts.models.forecasting.transformer_model.rst.txt
index cd095bca46..51c0eedce2 100644
--- a/_sources/generated_api/darts.models.forecasting.transformer_model.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.transformer_model.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.transformer_model
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.varima.rst.txt b/_sources/generated_api/darts.models.forecasting.varima.rst.txt
index 2482560c51..f2528375cb 100644
--- a/_sources/generated_api/darts.models.forecasting.varima.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.varima.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.varima
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.forecasting.xgboost.rst.txt b/_sources/generated_api/darts.models.forecasting.xgboost.rst.txt
index 2bf0997ce2..c9289839fe 100644
--- a/_sources/generated_api/darts.models.forecasting.xgboost.rst.txt
+++ b/_sources/generated_api/darts.models.forecasting.xgboost.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.forecasting.xgboost
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.models.utils.rst.txt b/_sources/generated_api/darts.models.utils.rst.txt
index 965ea6b3d8..471c46c805 100644
--- a/_sources/generated_api/darts.models.utils.rst.txt
+++ b/_sources/generated_api/darts.models.utils.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.models.utils
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.timeseries.rst.txt b/_sources/generated_api/darts.timeseries.rst.txt
index e3d2da6b2a..e73fc820a8 100644
--- a/_sources/generated_api/darts.timeseries.rst.txt
+++ b/_sources/generated_api/darts.timeseries.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.timeseries
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.utils.callbacks.rst.txt b/_sources/generated_api/darts.utils.callbacks.rst.txt
index d9c20bdeaf..06a1fe62e3 100644
--- a/_sources/generated_api/darts.utils.callbacks.rst.txt
+++ b/_sources/generated_api/darts.utils.callbacks.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.utils.callbacks
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.utils.data.horizon_based_dataset.rst.txt b/_sources/generated_api/darts.utils.data.horizon_based_dataset.rst.txt
index 0ff361ba87..5ca6e3d9dc 100644
--- a/_sources/generated_api/darts.utils.data.horizon_based_dataset.rst.txt
+++ b/_sources/generated_api/darts.utils.data.horizon_based_dataset.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.utils.data.horizon_based_dataset
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.utils.data.inference_dataset.rst.txt b/_sources/generated_api/darts.utils.data.inference_dataset.rst.txt
index fbcf0efc7f..c37ac987ea 100644
--- a/_sources/generated_api/darts.utils.data.inference_dataset.rst.txt
+++ b/_sources/generated_api/darts.utils.data.inference_dataset.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.utils.data.inference_dataset
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.utils.data.sequential_dataset.rst.txt b/_sources/generated_api/darts.utils.data.sequential_dataset.rst.txt
index 14ee5be67a..5363bfb87f 100644
--- a/_sources/generated_api/darts.utils.data.sequential_dataset.rst.txt
+++ b/_sources/generated_api/darts.utils.data.sequential_dataset.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.utils.data.sequential_dataset
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.utils.data.shifted_dataset.rst.txt b/_sources/generated_api/darts.utils.data.shifted_dataset.rst.txt
index 8ef4c4c83f..a37959f2d7 100644
--- a/_sources/generated_api/darts.utils.data.shifted_dataset.rst.txt
+++ b/_sources/generated_api/darts.utils.data.shifted_dataset.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.utils.data.shifted_dataset
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.utils.data.tabularization.rst.txt b/_sources/generated_api/darts.utils.data.tabularization.rst.txt
index 77e9a68e81..3f268ee18f 100644
--- a/_sources/generated_api/darts.utils.data.tabularization.rst.txt
+++ b/_sources/generated_api/darts.utils.data.tabularization.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.utils.data.tabularization
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.utils.data.training_dataset.rst.txt b/_sources/generated_api/darts.utils.data.training_dataset.rst.txt
index ed00f626f2..f9368e59bd 100644
--- a/_sources/generated_api/darts.utils.data.training_dataset.rst.txt
+++ b/_sources/generated_api/darts.utils.data.training_dataset.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.utils.data.training_dataset
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.utils.data.utils.rst.txt b/_sources/generated_api/darts.utils.data.utils.rst.txt
index e7dc5d3508..889e919d9c 100644
--- a/_sources/generated_api/darts.utils.data.utils.rst.txt
+++ b/_sources/generated_api/darts.utils.data.utils.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.utils.data.utils
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.utils.historical_forecasts.optimized_historical_forecasts_regression.rst.txt b/_sources/generated_api/darts.utils.historical_forecasts.optimized_historical_forecasts_regression.rst.txt
index e2891357e4..a650d5b263 100644
--- a/_sources/generated_api/darts.utils.historical_forecasts.optimized_historical_forecasts_regression.rst.txt
+++ b/_sources/generated_api/darts.utils.historical_forecasts.optimized_historical_forecasts_regression.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.utils.historical_forecasts.optimized_historical_forecasts_regression
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.utils.historical_forecasts.optimized_historical_forecasts_torch.rst.txt b/_sources/generated_api/darts.utils.historical_forecasts.optimized_historical_forecasts_torch.rst.txt
index 044e3fc260..ec4090f768 100644
--- a/_sources/generated_api/darts.utils.historical_forecasts.optimized_historical_forecasts_torch.rst.txt
+++ b/_sources/generated_api/darts.utils.historical_forecasts.optimized_historical_forecasts_torch.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.utils.historical_forecasts.optimized_historical_forecasts_torch
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.utils.historical_forecasts.utils.rst.txt b/_sources/generated_api/darts.utils.historical_forecasts.utils.rst.txt
index 8a46e62c5a..bcb9095084 100644
--- a/_sources/generated_api/darts.utils.historical_forecasts.utils.rst.txt
+++ b/_sources/generated_api/darts.utils.historical_forecasts.utils.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.utils.historical_forecasts.utils
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.utils.likelihood_models.rst.txt b/_sources/generated_api/darts.utils.likelihood_models.rst.txt
index ce887a261d..dd6781b44a 100644
--- a/_sources/generated_api/darts.utils.likelihood_models.rst.txt
+++ b/_sources/generated_api/darts.utils.likelihood_models.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.utils.likelihood_models
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.utils.losses.rst.txt b/_sources/generated_api/darts.utils.losses.rst.txt
index b41b1234bd..3dc95e81bf 100644
--- a/_sources/generated_api/darts.utils.losses.rst.txt
+++ b/_sources/generated_api/darts.utils.losses.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.utils.losses
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.utils.missing_values.rst.txt b/_sources/generated_api/darts.utils.missing_values.rst.txt
index 3782f7ff65..657aa3e6ae 100644
--- a/_sources/generated_api/darts.utils.missing_values.rst.txt
+++ b/_sources/generated_api/darts.utils.missing_values.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.utils.missing_values
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.utils.model_selection.rst.txt b/_sources/generated_api/darts.utils.model_selection.rst.txt
index f8899d5986..1c20e0c17d 100644
--- a/_sources/generated_api/darts.utils.model_selection.rst.txt
+++ b/_sources/generated_api/darts.utils.model_selection.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.utils.model_selection
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.utils.multioutput.rst.txt b/_sources/generated_api/darts.utils.multioutput.rst.txt
index b559f583eb..4bcab6098d 100644
--- a/_sources/generated_api/darts.utils.multioutput.rst.txt
+++ b/_sources/generated_api/darts.utils.multioutput.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.utils.multioutput
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.utils.statistics.rst.txt b/_sources/generated_api/darts.utils.statistics.rst.txt
index 90b13d579a..63baf96590 100644
--- a/_sources/generated_api/darts.utils.statistics.rst.txt
+++ b/_sources/generated_api/darts.utils.statistics.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.utils.statistics
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.utils.timeseries_generation.rst.txt b/_sources/generated_api/darts.utils.timeseries_generation.rst.txt
index 146884ff6d..9181d6b8f4 100644
--- a/_sources/generated_api/darts.utils.timeseries_generation.rst.txt
+++ b/_sources/generated_api/darts.utils.timeseries_generation.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.utils.timeseries_generation
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.utils.torch.rst.txt b/_sources/generated_api/darts.utils.torch.rst.txt
index c166409827..0341f085a5 100644
--- a/_sources/generated_api/darts.utils.torch.rst.txt
+++ b/_sources/generated_api/darts.utils.torch.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.utils.torch
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/generated_api/darts.utils.utils.rst.txt b/_sources/generated_api/darts.utils.utils.rst.txt
index 9a677bbcec..17852d3ef9 100644
--- a/_sources/generated_api/darts.utils.utils.rst.txt
+++ b/_sources/generated_api/darts.utils.utils.rst.txt
@@ -1,4 +1,3 @@
-
 .. automodule:: darts.utils.utils
    :members:
    :undoc-members:
\ No newline at end of file
diff --git a/_sources/quickstart/00-quickstart.ipynb.txt b/_sources/quickstart/00-quickstart.ipynb.txt
index 027ceb95e7..c4bf8a58f6 100644
--- a/_sources/quickstart/00-quickstart.ipynb.txt
+++ b/_sources/quickstart/00-quickstart.ipynb.txt
@@ -134,7 +134,7 @@
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -146,7 +146,7 @@
     }
    ],
    "source": [
-    "series1, series2 = series.split_before(0.75)\n",
+    "series1, series2 = series.split_after(0.75)\n",
     "series1.plot()\n",
     "series2.plot()"
    ]
@@ -2970,7 +2970,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.10.13"
   },
   "pycharm": {
    "stem_cell": {
diff --git a/examples/01-multi-time-series-and-covariates.html b/examples/01-multi-time-series-and-covariates.html
index 921407747f..f02dac6802 100644
--- a/examples/01-multi-time-series-and-covariates.html
+++ b/examples/01-multi-time-series-and-covariates.html
@@ -829,31 +829,28 @@ <h2>Train / Validation split<a class="headerlink" href="#Train-/-Validation-spli
 </div>
 <section id="Global-Forecasting-Models">
 <h3>Global Forecasting Models<a class="headerlink" href="#Global-Forecasting-Models" title="Permalink to this headline">¶</a></h3>
-<p>Darts contains many forecasting models, but not all of them can be trained on several time series. The models that support training on multiple series are called <em>global</em> models. At the time of writing, there are 5 global models:</p>
+<p>Darts contains many forecasting models, but not all of them can be trained on several time series. The models that support training on multiple series are called <em>global</em> models. An exhaustive list of the global models can be found <a class="reference external" href="https://unit8co.github.io/darts/README.html#forecasting-models">here</a> (bottom of the table) with for example:</p>
 <ul class="simple">
+<li><p>LinearRegressionModel</p></li>
 <li><p>BlockRNNModel</p></li>
-<li><p>RNNModel</p></li>
-<li><p>Temporal Convolutional Networks (TCNs)</p></li>
-<li><p>N-Beats</p></li>
-<li><p>Transformer model</p></li>
+<li><p>Temporal Convolutional Networks (TCNModel)</p></li>
+<li><p>N-Beats (NBEATSModel)</p></li>
+<li><p>TiDEModel</p></li>
 </ul>
 <p>In the following, we will distinguish two sorts of time series:</p>
 <ul class="simple">
 <li><p>The <strong>target time series</strong> is the time series we are interested to forecast (given its history)</p></li>
 <li><p>A <strong>covariate time series</strong> is a time series which may help in the forecasting of the target series, but that we are not interested in forecasting. It’s sometimes also called <em>external data</em>.</p></li>
 </ul>
+<p><strong>Note</strong>: Static covariates are invariant in time and correspond to additional information associated with the components of the <strong>target time series</strong>. Detailed information about this kind of covariate can be found in the <a class="reference external" href="https://unit8co.github.io/darts/examples/15-static-covariates.html">static covariates notebook</a>.</p>
 <p>We further differentiate covariates series, depending on whether they can be known in advance or not:</p>
 <ul class="simple">
 <li><p><strong>Past Covariates</strong> denote time series whose past values are known at prediction time. These are usually things that have to be measured or observed.</p></li>
 <li><p><strong>Future Covariates</strong> denote time series whose future values are already known at prediction time for the span of the forecast horizon. These can for instance represent known future holidays, or weather forecasts.</p></li>
 </ul>
-<p>Some models use only past covariates, others use only future covariates, and some models might use both. We will dive deeper in this topic in some other notebook, but for now it is enough to know this:</p>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">BlockRNNModel</span></code>, <code class="docutils literal notranslate"><span class="pre">TCNModel</span></code>, <code class="docutils literal notranslate"><span class="pre">NBEATSModel</span></code> and <code class="docutils literal notranslate"><span class="pre">TransformerModel</span></code> all use <code class="docutils literal notranslate"><span class="pre">past_covariates</span></code>.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">RNNModel</span></code> uses <code class="docutils literal notranslate"><span class="pre">future_covariates</span></code>.</p></li>
-</ul>
+<p>Some models use only past covariates, others use only future covariates, and some models might use both. We will dive deeper in this topic in some other notebook, but this <a class="reference external" href="https://unit8co.github.io/darts/README.html#forecasting-models">table</a> details the covariates supported by each model.</p>
 <p>All of the global models listed above support training on multiple series. In addition, they also all support <em>multivariate series</em>. This means that they can seamlessly be used with time series of more than one dimension; the target series can contain one (as is often the case) or several dimensions. A time series with several dimensions is really just a regular time series where the values at each time stamps are vectors instead of scalars.</p>
-<p>As an example, the 4 models supporting <code class="docutils literal notranslate"><span class="pre">past_covariates</span></code> follow a “block” architecture. They contain a neural network that takes chunks of time series in input, and outputs chunks of (predicted) future time series values. The input dimensionality is the number of dimensions (components) of the target series, plus the number of components of all the covariates - stacked together. The output dimensionality is simply the number of dimensions of the target series: <img alt="image0" src="../_images/seq_dataset_one_ts.png" /></p>
+<p>As an example, the BlockRNNModel, N-Beats, TCN and Transformer models follow a “block” architecture. They contain a neural network that takes chunks of time series in input, and outputs chunks of (predicted) future time series values. The input dimensionality is the number of dimensions (components) of the target series, plus the number of components of all the covariates - stacked together. The output dimensionality is simply the number of dimensions of the target series: <img alt="image0" src="../_images/seq_dataset_one_ts.png" /></p>
 <p>The <code class="docutils literal notranslate"><span class="pre">RNNModel</span></code> works differently, in a recurrent fashion (which is also why they support future covariates). The good news is that as a user, we don’t have to worry too much about the different model types and input/output dimensionalities. The dimensionalities are automatically inferred for us by the model based on the training data, and the support for past or future covariates is simply handled by the <code class="docutils literal notranslate"><span class="pre">past_covariates</span></code> or <code class="docutils literal notranslate"><span class="pre">future_covariates</span></code> arguments.</p>
 <p>We’ll still have to specify two important parameters when building our models:</p>
 <ul class="simple">
diff --git a/examples/01-multi-time-series-and-covariates.ipynb b/examples/01-multi-time-series-and-covariates.ipynb
index f3dea1d1bb..8744bb69a7 100644
--- a/examples/01-multi-time-series-and-covariates.ipynb
+++ b/examples/01-multi-time-series-and-covariates.ipynb
@@ -167,32 +167,31 @@
    "metadata": {},
    "source": [
     "## Global Forecasting Models\n",
-    "Darts contains many forecasting models, but not all of them can be trained on several time series. The models that support training on multiple series are called *global* models. At the time of writing, there are 5 global models:\n",
+    "Darts contains many forecasting models, but not all of them can be trained on several time series. The models that support training on multiple series are called *global* models. An exhaustive list of the global models can be found [here](https://unit8co.github.io/darts/README.html#forecasting-models) (bottom of the table) with for example:\n",
     "\n",
+    "* LinearRegressionModel\n",
     "* BlockRNNModel\n",
-    "* RNNModel\n",
-    "* Temporal Convolutional Networks (TCNs)\n",
-    "* N-Beats\n",
-    "* Transformer model\n",
+    "* Temporal Convolutional Networks (TCNModel)\n",
+    "* N-Beats (NBEATSModel)\n",
+    "* TiDEModel\n",
     "\n",
     "In the following, we will distinguish two sorts of time series:\n",
     "\n",
     "* The **target time series** is the time series we are interested to forecast (given its history)\n",
     "* A **covariate time series** is a time series which may help in the forecasting of the target series, but that we are not interested in forecasting. It's sometimes also called *external data*.\n",
     "\n",
+    "**Note**: Static covariates are invariant in time and correspond to additional information associated with the components of the **target time series**. Detailed information about this kind of covariate can be found in the [static covariates notebook](https://unit8co.github.io/darts/examples/15-static-covariates.html).\n",
+    "\n",
     "We further differentiate covariates series, depending on whether they can be known in advance or not:\n",
     "\n",
     "* **Past Covariates** denote time series whose past values are known at prediction time. These are usually things that have to be measured or observed.\n",
     "* **Future Covariates** denote time series whose future values are already known at prediction time for the span of the forecast horizon. These can for instance represent known future holidays, or weather forecasts. \n",
     "\n",
-    "Some models use only past covariates, others use only future covariates, and some models might use both. We will dive deeper in this topic in some other notebook, but for now it is enough to know this:\n",
-    "\n",
-    "* `BlockRNNModel`, `TCNModel`, `NBEATSModel` and `TransformerModel` all use `past_covariates`.\n",
-    "* `RNNModel` uses `future_covariates`.\n",
+    "Some models use only past covariates, others use only future covariates, and some models might use both. We will dive deeper in this topic in some other notebook, but this [table](https://unit8co.github.io/darts/README.html#forecasting-models) details the covariates supported by each model.\n",
     "\n",
-    "All of the global models listed above support training on multiple series. In addition, they also all support *multivariate series*. This means that they can seamlessly be used with time series of more than one dimension; the target series can contain one (as is often the case) or several dimensions. A time series with several dimensions is  really just a regular time series where the values at each time stamps are vectors instead of scalars.\n",
+    "All of the global models listed above support training on multiple series. In addition, they also all support *multivariate series*. This means that they can seamlessly be used with time series of more than one dimension; the target series can contain one (as is often the case) or several dimensions. A time series with several dimensions is really just a regular time series where the values at each time stamps are vectors instead of scalars.\n",
     "\n",
-    "As an example, the 4 models supporting `past_covariates` follow a \"block\" architecture. They contain a neural network that takes chunks of time series in input, and outputs chunks of (predicted) future time series values. The input dimensionality is the number of dimensions (components) of the target series, plus the number of components of all the covariates - stacked together. The output dimensionality is simply the number of dimensions of the target series:\n",
+    "As an example, the BlockRNNModel, N-Beats, TCN and Transformer models follow a \"block\" architecture. They contain a neural network that takes chunks of time series in input, and outputs chunks of (predicted) future time series values. The input dimensionality is the number of dimensions (components) of the target series, plus the number of components of all the covariates - stacked together. The output dimensionality is simply the number of dimensions of the target series:\n",
     "![](static/images/global_io_covs.png)\n",
     "\n",
     "The `RNNModel` works differently, in a recurrent fashion (which is also why they support future covariates).\n",
diff --git a/examples/14-transfer-learning.ipynb b/examples/14-transfer-learning.ipynb
index 02c141ad67..df7d65bdbc 100644
--- a/examples/14-transfer-learning.ipynb
+++ b/examples/14-transfer-learning.ipynb
@@ -33,7 +33,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "be286424",
+   "id": "58072d73",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/examples/20-RegressionModel-examples.html b/examples/20-RegressionModel-examples.html
index ca930f993b..bf8cb3eb36 100644
--- a/examples/20-RegressionModel-examples.html
+++ b/examples/20-RegressionModel-examples.html
@@ -1085,7 +1085,7 @@ <h2>Component-specific lags<a class="headerlink" href="#Component-specific-lags"
 <section id="Model’s-output-chunk-length">
 <h2>Model’s output chunk length<a class="headerlink" href="#Model’s-output-chunk-length" title="Permalink to this headline">¶</a></h2>
 <p>This key parameter sets the <em>number of time steps that can be predicted at once by the internal regression model</em>.</p>
-<p>It is not the same as forecast horizon <code class="docutils literal notranslate"><span class="pre">n</span></code> from <code class="docutils literal notranslate"><span class="pre">predict()</span></code> which is the <strong>desired</strong> the number of generated prediction points, that is achieved either with: - a single shot forecast (if <code class="docutils literal notranslate"><span class="pre">output_chunk_length</span> <span class="pre">&lt;=</span> <span class="pre">n</span></code>), or - an auto-regressive forecast, consuming its own forecast (and future values of covariates) as input for additional predictions (otherwise)</p>
+<p>It is not the same as forecast horizon <code class="docutils literal notranslate"><span class="pre">n</span></code> from <code class="docutils literal notranslate"><span class="pre">predict()</span></code> which is the <strong>desired</strong> the number of generated prediction points, that is achieved either with: - a single shot forecast (if <code class="docutils literal notranslate"><span class="pre">n</span> <span class="pre">&lt;=</span> <span class="pre">output_chunk_length</span></code>), or - an auto-regressive forecast, consuming its own forecast (and future values of covariates) as input for additional predictions (otherwise)</p>
 <p>For example, if we want our model to forecast the next 24 hours of electricity consumption based on the last day of consumption, setting <code class="docutils literal notranslate"><span class="pre">output_chunk_length=24</span></code> ensures that the model will not consume its forecasts or future values of our covariates to predict the entire day.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
diff --git a/examples/20-RegressionModel-examples.ipynb b/examples/20-RegressionModel-examples.ipynb
index a63b2514de..30a84de818 100644
--- a/examples/20-RegressionModel-examples.ipynb
+++ b/examples/20-RegressionModel-examples.ipynb
@@ -502,7 +502,7 @@
     "This key parameter sets the *number of time steps that can be predicted at once by the internal regression model*.\n",
     "\n",
     "It is not the same as forecast horizon `n` from `predict()` which is the **desired** the number of generated prediction points, that is achieved either with:\n",
-    "- a single shot forecast (if `output_chunk_length <= n`), or\n",
+    "- a single shot forecast (if `n <= output_chunk_length`), or\n",
     "- an auto-regressive forecast, consuming its own forecast (and future values of covariates) as input for additional predictions (otherwise)\n",
     "\n",
     "For example, if we want our model to forecast the next 24 hours of electricity consumption based on the last day of consumption, setting `output_chunk_length=24` ensures that the model will not consume its forecasts or future values of our covariates to predict the entire day."
diff --git a/generated_api/darts.dataprocessing.transformers.base_data_transformer.html b/generated_api/darts.dataprocessing.transformers.base_data_transformer.html
index 26bfcff3f9..7ce2a2fbb4 100644
--- a/generated_api/darts.dataprocessing.transformers.base_data_transformer.html
+++ b/generated_api/darts.dataprocessing.transformers.base_data_transformer.html
@@ -880,7 +880,7 @@ <h1>Data Transformer Base Class<a class="headerlink" href="#data-transformer-bas
 parallel job. If <cite>parallel_params=False</cite>, every fixed parameter will take on the same value for
 each parallel job. If <cite>parallel_params</cite> is a <cite>Sequence</cite> of fixed attribute names, only those
 attribute names specified will take on different values between different parallel jobs.</p></li>
-<li><p><strong>mask_components</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, whether or not to automatically apply any provided <cite>component_mask`s to the
+<li><p><strong>mask_components</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, whether to automatically apply any provided <cite>component_mask`s to the
 `TimeSeries</cite> inputs passed to <cite>transform</cite>, <cite>fit</cite>, <cite>inverse_transform</cite>, or <cite>fit_transform</cite>.
 If <cite>True</cite>, any specified <cite>component_mask</cite> will be applied to each input timeseries
 before passing them to the called method; the masked components will also be automatically
diff --git a/generated_api/darts.dataprocessing.transformers.fittable_data_transformer.html b/generated_api/darts.dataprocessing.transformers.fittable_data_transformer.html
index ee2f3534cd..e8643e8307 100644
--- a/generated_api/darts.dataprocessing.transformers.fittable_data_transformer.html
+++ b/generated_api/darts.dataprocessing.transformers.fittable_data_transformer.html
@@ -861,7 +861,7 @@ <h1>Fittable Data Transformer Base Class<a class="headerlink" href="#fittable-da
 parallel job. If <cite>parallel_params=False</cite>, every fixed parameter will take on the same value for
 each parallel job. If <cite>parallel_params</cite> is a <cite>Sequence</cite> of fixed attribute names, only those
 attribute names specified will take on different values between different parallel jobs.</p></li>
-<li><p><strong>mask_components</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, whether or not to automatically apply any provided <cite>component_mask`s to the
+<li><p><strong>mask_components</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, whether to automatically apply any provided <cite>component_mask`s to the
 `TimeSeries</cite> inputs passed to <cite>transform</cite>, <cite>fit</cite>, <cite>inverse_transform</cite>, or <cite>fit_transform</cite>.
 If <cite>True</cite>, any specified <cite>component_mask</cite> will be applied to each input timeseries
 before passing them to the called method; the masked components will also be automatically
diff --git a/generated_api/darts.dataprocessing.transformers.invertible_data_transformer.html b/generated_api/darts.dataprocessing.transformers.invertible_data_transformer.html
index c386a6cdd9..f711cca9c3 100644
--- a/generated_api/darts.dataprocessing.transformers.invertible_data_transformer.html
+++ b/generated_api/darts.dataprocessing.transformers.invertible_data_transformer.html
@@ -869,7 +869,7 @@ <h1>Invertible Data Transformer Base Class<a class="headerlink" href="#invertibl
 parallel job. If <cite>parallel_params=False</cite>, every fixed parameter will take on the same value for
 each parallel job. If <cite>parallel_params</cite> is a <cite>Sequence</cite> of fixed attribute names, only those
 attribute names specified will take on different values between different parallel jobs.</p></li>
-<li><p><strong>mask_components</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, whether or not to automatically apply any provided <cite>component_mask`s to the
+<li><p><strong>mask_components</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, whether to automatically apply any provided <cite>component_mask`s to the
 `TimeSeries</cite> inputs passed to <cite>transform</cite>, <cite>fit</cite>, <cite>inverse_transform</cite>, or <cite>fit_transform</cite>.
 If <cite>True</cite>, any specified <cite>component_mask</cite> will be applied to each input timeseries
 before passing them to the called method; the masked components will also be automatically
diff --git a/generated_api/darts.dataprocessing.transformers.reconciliation.html b/generated_api/darts.dataprocessing.transformers.reconciliation.html
index ff2a21c435..f9098d2f8c 100644
--- a/generated_api/darts.dataprocessing.transformers.reconciliation.html
+++ b/generated_api/darts.dataprocessing.transformers.reconciliation.html
@@ -941,7 +941,7 @@ <h1>Hierarchical Reconciliation<a class="headerlink" href="#hierarchical-reconci
 parallel job. If <cite>parallel_params=False</cite>, every fixed parameter will take on the same value for
 each parallel job. If <cite>parallel_params</cite> is a <cite>Sequence</cite> of fixed attribute names, only those
 attribute names specified will take on different values between different parallel jobs.</p></li>
-<li><p><strong>mask_components</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, whether or not to automatically apply any provided <cite>component_mask`s to the
+<li><p><strong>mask_components</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, whether to automatically apply any provided <cite>component_mask`s to the
 `TimeSeries</cite> inputs passed to <cite>transform</cite>, <cite>fit</cite>, <cite>inverse_transform</cite>, or <cite>fit_transform</cite>.
 If <cite>True</cite>, any specified <cite>component_mask</cite> will be applied to each input timeseries
 before passing them to the called method; the masked components will also be automatically
@@ -1879,7 +1879,7 @@ <h1>Hierarchical Reconciliation<a class="headerlink" href="#hierarchical-reconci
 parallel job. If <cite>parallel_params=False</cite>, every fixed parameter will take on the same value for
 each parallel job. If <cite>parallel_params</cite> is a <cite>Sequence</cite> of fixed attribute names, only those
 attribute names specified will take on different values between different parallel jobs.</p></li>
-<li><p><strong>mask_components</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, whether or not to automatically apply any provided <cite>component_mask`s to the
+<li><p><strong>mask_components</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, whether to automatically apply any provided <cite>component_mask`s to the
 `TimeSeries</cite> inputs passed to <cite>transform</cite>, <cite>fit</cite>, <cite>inverse_transform</cite>, or <cite>fit_transform</cite>.
 If <cite>True</cite>, any specified <cite>component_mask</cite> will be applied to each input timeseries
 before passing them to the called method; the masked components will also be automatically
diff --git a/generated_api/darts.models.forecasting.arima.html b/generated_api/darts.models.forecasting.arima.html
index cdbce81a92..2454232978 100644
--- a/generated_api/darts.models.forecasting.arima.html
+++ b/generated_api/darts.models.forecasting.arima.html
@@ -941,13 +941,16 @@ <h1>ARIMA<a class="headerlink" href="#arima" title="Permalink to this headline">
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.arima.ARIMA.supports_static_covariates" title="darts.models.forecasting.arima.ARIMA.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.arima.ARIMA.uses_future_covariates" title="darts.models.forecasting.arima.ARIMA.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.arima.ARIMA.supports_transferrable_series_prediction" title="darts.models.forecasting.arima.ARIMA.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.arima.ARIMA.uses_future_covariates" title="darts.models.forecasting.arima.ARIMA.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.arima.ARIMA.uses_past_covariates" title="darts.models.forecasting.arima.ARIMA.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.arima.ARIMA.uses_past_covariates" title="darts.models.forecasting.arima.ARIMA.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.arima.ARIMA.uses_static_covariates" title="darts.models.forecasting.arima.ARIMA.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.arima.ARIMA.uses_static_covariates" title="darts.models.forecasting.arima.ARIMA.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -1540,7 +1543,7 @@ <h1>ARIMA<a class="headerlink" href="#arima" title="Permalink to this headline">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.arima.ARIMA.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">series</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.arima.ARIMA.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">series</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.arima.ARIMA.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>If the <cite>series</cite> parameter is not set, forecasts values for <cite>n</cite> time steps after the end of the training
 series. If some future covariates were specified during the training, they must also be specified here.</p>
 <p>If the <cite>series</cite> parameter is set, forecasts values for <cite>n</cite> time steps after the end of the new target
@@ -1559,6 +1562,8 @@ <h1>ARIMA<a class="headerlink" href="#arima" title="Permalink to this headline">
 </p></li>
 <li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, set the prediction verbosity. Not effective for all models.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -1700,6 +1705,17 @@ <h1>ARIMA<a class="headerlink" href="#arima" title="Permalink to this headline">
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.arima.ARIMA.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.arima.ARIMA.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.arima.ARIMA.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.arima.ARIMA.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.auto_arima.html b/generated_api/darts.models.forecasting.auto_arima.html
index deca189a6d..9124fdc70f 100644
--- a/generated_api/darts.models.forecasting.auto_arima.html
+++ b/generated_api/darts.models.forecasting.auto_arima.html
@@ -935,13 +935,16 @@ <h1>AutoARIMA<a class="headerlink" href="#autoarima" title="Permalink to this he
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.auto_arima.AutoARIMA.supports_static_covariates" title="darts.models.forecasting.auto_arima.AutoARIMA.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.auto_arima.AutoARIMA.uses_future_covariates" title="darts.models.forecasting.auto_arima.AutoARIMA.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.auto_arima.AutoARIMA.supports_transferrable_series_prediction" title="darts.models.forecasting.auto_arima.AutoARIMA.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.auto_arima.AutoARIMA.uses_future_covariates" title="darts.models.forecasting.auto_arima.AutoARIMA.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.auto_arima.AutoARIMA.uses_past_covariates" title="darts.models.forecasting.auto_arima.AutoARIMA.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.auto_arima.AutoARIMA.uses_past_covariates" title="darts.models.forecasting.auto_arima.AutoARIMA.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.auto_arima.AutoARIMA.uses_static_covariates" title="darts.models.forecasting.auto_arima.AutoARIMA.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.auto_arima.AutoARIMA.uses_static_covariates" title="darts.models.forecasting.auto_arima.AutoARIMA.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -988,7 +991,7 @@ <h1>AutoARIMA<a class="headerlink" href="#autoarima" title="Permalink to this he
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.auto_arima.AutoARIMA.load" title="darts.models.forecasting.auto_arima.AutoARIMA.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path)</p></td>
 <td><p>Loads the model from a given path or file handle.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.auto_arima.AutoARIMA.predict" title="darts.models.forecasting.auto_arima.AutoARIMA.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, future_covariates, num_samples])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.auto_arima.AutoARIMA.predict" title="darts.models.forecasting.auto_arima.AutoARIMA.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, future_covariates, num_samples, ...])</p></td>
 <td><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.auto_arima.AutoARIMA.residuals" title="darts.models.forecasting.auto_arima.AutoARIMA.residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">residuals</span></code></a>(series[, past_covariates, ...])</p></td>
@@ -1534,7 +1537,7 @@ <h1>AutoARIMA<a class="headerlink" href="#autoarima" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.auto_arima.AutoARIMA.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.auto_arima.AutoARIMA.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.auto_arima.AutoARIMA.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p>
 <p>If some future covariates were specified during the training, they must also be specified here.</p>
 <dl class="field-list simple">
@@ -1546,6 +1549,8 @@ <h1>AutoARIMA<a class="headerlink" href="#autoarima" title="Permalink to this he
 contain at least the next <cite>n</cite> time steps/indices after the end of the training target series.</p></li>
 <li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, set the prediction verbosity. Not effective for all models.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -1687,6 +1692,17 @@ <h1>AutoARIMA<a class="headerlink" href="#autoarima" title="Permalink to this he
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.auto_arima.AutoARIMA.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.auto_arima.AutoARIMA.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.auto_arima.AutoARIMA.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.auto_arima.AutoARIMA.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.baselines.html b/generated_api/darts.models.forecasting.baselines.html
index 53e6a046d5..6f6863a48a 100644
--- a/generated_api/darts.models.forecasting.baselines.html
+++ b/generated_api/darts.models.forecasting.baselines.html
@@ -899,13 +899,16 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveDrift.supports_static_covariates" title="darts.models.forecasting.baselines.NaiveDrift.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveDrift.uses_future_covariates" title="darts.models.forecasting.baselines.NaiveDrift.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveDrift.supports_transferrable_series_prediction" title="darts.models.forecasting.baselines.NaiveDrift.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveDrift.uses_future_covariates" title="darts.models.forecasting.baselines.NaiveDrift.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveDrift.uses_past_covariates" title="darts.models.forecasting.baselines.NaiveDrift.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveDrift.uses_past_covariates" title="darts.models.forecasting.baselines.NaiveDrift.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveDrift.uses_static_covariates" title="darts.models.forecasting.baselines.NaiveDrift.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveDrift.uses_static_covariates" title="darts.models.forecasting.baselines.NaiveDrift.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -952,7 +955,7 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveDrift.load" title="darts.models.forecasting.baselines.NaiveDrift.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path)</p></td>
 <td><p>Loads the model from a given path or file handle.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveDrift.predict" title="darts.models.forecasting.baselines.NaiveDrift.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveDrift.predict" title="darts.models.forecasting.baselines.NaiveDrift.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose, show_warnings])</p></td>
 <td><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveDrift.residuals" title="darts.models.forecasting.baselines.NaiveDrift.residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">residuals</span></code></a>(series[, past_covariates, ...])</p></td>
@@ -1492,7 +1495,7 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.baselines.NaiveDrift.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/baselines.html#NaiveDrift.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.baselines.NaiveDrift.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/baselines.html#NaiveDrift.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.baselines.NaiveDrift.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1500,6 +1503,8 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 <li><p><strong>n</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Forecast horizon - the number of time steps after the end of the series for which to produce predictions.</p></li>
 <li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, set the prediction verbosity. Not effective for all models.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -1641,6 +1646,17 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.baselines.NaiveDrift.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.baselines.NaiveDrift.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.baselines.NaiveDrift.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.baselines.NaiveDrift.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
@@ -1750,13 +1766,16 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveEnsembleModel.supports_static_covariates" title="darts.models.forecasting.baselines.NaiveEnsembleModel.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveEnsembleModel.uses_future_covariates" title="darts.models.forecasting.baselines.NaiveEnsembleModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveEnsembleModel.supports_transferrable_series_prediction" title="darts.models.forecasting.baselines.NaiveEnsembleModel.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveEnsembleModel.uses_future_covariates" title="darts.models.forecasting.baselines.NaiveEnsembleModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveEnsembleModel.uses_past_covariates" title="darts.models.forecasting.baselines.NaiveEnsembleModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveEnsembleModel.uses_past_covariates" title="darts.models.forecasting.baselines.NaiveEnsembleModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveEnsembleModel.uses_static_covariates" title="darts.models.forecasting.baselines.NaiveEnsembleModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveEnsembleModel.uses_static_covariates" title="darts.models.forecasting.baselines.NaiveEnsembleModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -2524,6 +2543,17 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.baselines.NaiveEnsembleModel.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.baselines.NaiveEnsembleModel.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.baselines.NaiveEnsembleModel.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.baselines.NaiveEnsembleModel.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
@@ -2619,13 +2649,16 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMean.supports_static_covariates" title="darts.models.forecasting.baselines.NaiveMean.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMean.uses_future_covariates" title="darts.models.forecasting.baselines.NaiveMean.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMean.supports_transferrable_series_prediction" title="darts.models.forecasting.baselines.NaiveMean.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMean.uses_future_covariates" title="darts.models.forecasting.baselines.NaiveMean.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMean.uses_past_covariates" title="darts.models.forecasting.baselines.NaiveMean.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMean.uses_past_covariates" title="darts.models.forecasting.baselines.NaiveMean.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMean.uses_static_covariates" title="darts.models.forecasting.baselines.NaiveMean.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMean.uses_static_covariates" title="darts.models.forecasting.baselines.NaiveMean.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -2672,7 +2705,7 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMean.load" title="darts.models.forecasting.baselines.NaiveMean.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path)</p></td>
 <td><p>Loads the model from a given path or file handle.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMean.predict" title="darts.models.forecasting.baselines.NaiveMean.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMean.predict" title="darts.models.forecasting.baselines.NaiveMean.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose, show_warnings])</p></td>
 <td><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMean.residuals" title="darts.models.forecasting.baselines.NaiveMean.residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">residuals</span></code></a>(series[, past_covariates, ...])</p></td>
@@ -3212,7 +3245,7 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.baselines.NaiveMean.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/baselines.html#NaiveMean.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.baselines.NaiveMean.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/baselines.html#NaiveMean.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.baselines.NaiveMean.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -3220,6 +3253,8 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 <li><p><strong>n</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Forecast horizon - the number of time steps after the end of the series for which to produce predictions.</p></li>
 <li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, set the prediction verbosity. Not effective for all models.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -3361,6 +3396,17 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.baselines.NaiveMean.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.baselines.NaiveMean.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.baselines.NaiveMean.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.baselines.NaiveMean.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
@@ -3460,13 +3506,16 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMovingAverage.supports_static_covariates" title="darts.models.forecasting.baselines.NaiveMovingAverage.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMovingAverage.uses_future_covariates" title="darts.models.forecasting.baselines.NaiveMovingAverage.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMovingAverage.supports_transferrable_series_prediction" title="darts.models.forecasting.baselines.NaiveMovingAverage.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMovingAverage.uses_future_covariates" title="darts.models.forecasting.baselines.NaiveMovingAverage.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMovingAverage.uses_past_covariates" title="darts.models.forecasting.baselines.NaiveMovingAverage.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMovingAverage.uses_past_covariates" title="darts.models.forecasting.baselines.NaiveMovingAverage.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMovingAverage.uses_static_covariates" title="darts.models.forecasting.baselines.NaiveMovingAverage.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMovingAverage.uses_static_covariates" title="darts.models.forecasting.baselines.NaiveMovingAverage.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -3513,7 +3562,7 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMovingAverage.load" title="darts.models.forecasting.baselines.NaiveMovingAverage.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path)</p></td>
 <td><p>Loads the model from a given path or file handle.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMovingAverage.predict" title="darts.models.forecasting.baselines.NaiveMovingAverage.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMovingAverage.predict" title="darts.models.forecasting.baselines.NaiveMovingAverage.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose, show_warnings])</p></td>
 <td><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveMovingAverage.residuals" title="darts.models.forecasting.baselines.NaiveMovingAverage.residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">residuals</span></code></a>(series[, past_covariates, ...])</p></td>
@@ -4053,7 +4102,7 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.baselines.NaiveMovingAverage.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/baselines.html#NaiveMovingAverage.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.baselines.NaiveMovingAverage.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/baselines.html#NaiveMovingAverage.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.baselines.NaiveMovingAverage.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -4061,6 +4110,8 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 <li><p><strong>n</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Forecast horizon - the number of time steps after the end of the series for which to produce predictions.</p></li>
 <li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, set the prediction verbosity. Not effective for all models.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -4202,6 +4253,17 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.baselines.NaiveMovingAverage.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.baselines.NaiveMovingAverage.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.baselines.NaiveMovingAverage.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.baselines.NaiveMovingAverage.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
@@ -4304,13 +4366,16 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveSeasonal.supports_static_covariates" title="darts.models.forecasting.baselines.NaiveSeasonal.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveSeasonal.uses_future_covariates" title="darts.models.forecasting.baselines.NaiveSeasonal.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveSeasonal.supports_transferrable_series_prediction" title="darts.models.forecasting.baselines.NaiveSeasonal.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveSeasonal.uses_future_covariates" title="darts.models.forecasting.baselines.NaiveSeasonal.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveSeasonal.uses_past_covariates" title="darts.models.forecasting.baselines.NaiveSeasonal.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveSeasonal.uses_past_covariates" title="darts.models.forecasting.baselines.NaiveSeasonal.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveSeasonal.uses_static_covariates" title="darts.models.forecasting.baselines.NaiveSeasonal.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveSeasonal.uses_static_covariates" title="darts.models.forecasting.baselines.NaiveSeasonal.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -4357,7 +4422,7 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveSeasonal.load" title="darts.models.forecasting.baselines.NaiveSeasonal.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path)</p></td>
 <td><p>Loads the model from a given path or file handle.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveSeasonal.predict" title="darts.models.forecasting.baselines.NaiveSeasonal.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveSeasonal.predict" title="darts.models.forecasting.baselines.NaiveSeasonal.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose, show_warnings])</p></td>
 <td><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.baselines.NaiveSeasonal.residuals" title="darts.models.forecasting.baselines.NaiveSeasonal.residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">residuals</span></code></a>(series[, past_covariates, ...])</p></td>
@@ -4897,7 +4962,7 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.baselines.NaiveSeasonal.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/baselines.html#NaiveSeasonal.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.baselines.NaiveSeasonal.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/baselines.html#NaiveSeasonal.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.baselines.NaiveSeasonal.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -4905,6 +4970,8 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 <li><p><strong>n</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Forecast horizon - the number of time steps after the end of the series for which to produce predictions.</p></li>
 <li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, set the prediction verbosity. Not effective for all models.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -5046,6 +5113,17 @@ <h1>Baseline Models<a class="headerlink" href="#baseline-models" title="Permalin
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.baselines.NaiveSeasonal.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.baselines.NaiveSeasonal.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.baselines.NaiveSeasonal.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.baselines.NaiveSeasonal.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.block_rnn_model.html b/generated_api/darts.models.forecasting.block_rnn_model.html
index 11d4b6da88..994b3bde6f 100644
--- a/generated_api/darts.models.forecasting.block_rnn_model.html
+++ b/generated_api/darts.models.forecasting.block_rnn_model.html
@@ -834,7 +834,7 @@
               
               <div>
                 
-  <span class="target" id="module-darts.models.forecasting.block_rnn_model"></span><section id="block-recurrent-neural-networks">
+  <section id="block-recurrent-neural-networks">
 <h1>Block Recurrent Neural Networks<a class="headerlink" href="#block-recurrent-neural-networks" title="Permalink to this headline">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="darts.models.forecasting.block_rnn_model.BlockRNNModel">
@@ -862,9 +862,8 @@ <h1>Block Recurrent Neural Networks<a class="headerlink" href="#block-recurrent-
 auto-regression. This is useful when the covariates don’t extend far enough into the future, or to prohibit
 the model from using future values of past and / or future covariates for prediction (depending on the
 model’s covariate support).</p></li>
-<li><p><strong>model</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Union</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code>]) – Either a string specifying the RNN module type (“RNN”, “LSTM” or “GRU”),
-or a PyTorch module with the same specifications as
-<code class="xref py py-class docutils literal notranslate"><span class="pre">darts.models.block_rnn_model._BlockRNNModule</span></code>.</p></li>
+<li><p><strong>model</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Union</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">Type</span></code>[<a class="reference internal" href="#darts.models.forecasting.block_rnn_model.CustomBlockRNNModule" title="darts.models.forecasting.block_rnn_model.CustomBlockRNNModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">CustomBlockRNNModule</span></code></a>]]) – Either a string specifying the RNN module type (“RNN”, “LSTM” or “GRU”), or a subclass of
+<a class="reference internal" href="#darts.models.forecasting.block_rnn_model.CustomBlockRNNModule" title="darts.models.forecasting.block_rnn_model.CustomBlockRNNModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">CustomBlockRNNModule</span></code></a> (the class itself, not an object of the class) with a custom logic.</p></li>
 <li><p><strong>hidden_dim</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Size for feature maps for each hidden RNN layer (<span class="math notranslate nohighlight">\(h_n\)</span>).
 In Darts version &lt;= 0.21, hidden_dim was referred as hidden_size.</p></li>
 <li><p><strong>n_rnn_layers</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of layers in the RNN module.</p></li>
@@ -903,7 +902,7 @@ <h1>Block Recurrent Neural Networks<a class="headerlink" href="#block-recurrent-
 <code class="docutils literal notranslate"><span class="pre">TimeSeries</span></code> is passed to the <a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.fit" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">fit()</span></code></a> method). Default: <code class="docutils literal notranslate"><span class="pre">1</span></code>.</p></li>
 <li><p><strong>force_reset</strong> – If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, any previously-existing model with the same name will be reset (all checkpoints will
 be discarded). Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
-<li><p><strong>save_checkpoints</strong> – Whether or not to automatically save the untrained model and checkpoints from training.
+<li><p><strong>save_checkpoints</strong> – Whether to automatically save the untrained model and checkpoints from training.
 To load the model from checkpoint, call <code class="xref py py-func docutils literal notranslate"><span class="pre">MyModelClass.load_from_checkpoint()</span></code>, where
 <code class="xref py py-class docutils literal notranslate"><span class="pre">MyModelClass</span></code> is the <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> class that was used (such as <code class="xref py py-class docutils literal notranslate"><span class="pre">TFTModel</span></code>,
 <code class="xref py py-class docutils literal notranslate"><span class="pre">NBEATSModel</span></code>, etc.). If set to <code class="docutils literal notranslate"><span class="pre">False</span></code>, the model can still be manually saved using
@@ -932,8 +931,8 @@ <h1>Block Recurrent Neural Networks<a class="headerlink" href="#block-recurrent-
 <li><p><strong>random_state</strong> – Control the randomness of the weights initialization. Check this
 <a class="reference external" href="https://scikit-learn.org/stable/glossary.html#term-random_state">link</a> for more details.
 Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li>
-<li><p><strong>pl_trainer_kwargs</strong> – <dl class="simple">
-<dt>By default <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> creates a PyTorch Lightning Trainer with several useful presets</dt><dd><p>that performs the training, validation and prediction processes. These presets include automatic
+<li><p><strong>pl_trainer_kwargs</strong> – <p>By default <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> creates a PyTorch Lightning Trainer with several useful presets
+that performs the training, validation and prediction processes. These presets include automatic
 checkpointing, tensorboard logging, setting the torch device and more.
 With <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code> you can add additional kwargs to instantiate the PyTorch Lightning trainer
 object. Check the <a class="reference external" href="https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html">PL Trainer documentation</a> for more information about the
@@ -941,8 +940,6 @@ <h1>Block Recurrent Neural Networks<a class="headerlink" href="#block-recurrent-
 Running on GPU(s) is also possible using <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code> by specifying keys <code class="docutils literal notranslate"><span class="pre">&quot;accelerator&quot;,</span>
 <span class="pre">&quot;devices&quot;,</span> <span class="pre">and</span> <span class="pre">&quot;auto_select_gpus&quot;</span></code>. Some examples for setting the devices inside the <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code>
 dict:</p>
-</dd>
-</dl>
 <ul>
 <li><p><code class="docutils literal notranslate"><span class="pre">{&quot;accelerator&quot;:</span> <span class="pre">&quot;cpu&quot;}</span></code> for CPU,</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">{&quot;accelerator&quot;:</span> <span class="pre">&quot;gpu&quot;,</span> <span class="pre">&quot;devices&quot;:</span> <span class="pre">[i]}</span></code> to use only GPU <code class="docutils literal notranslate"><span class="pre">i</span></code> (<code class="docutils literal notranslate"><span class="pre">i</span></code> must be an integer),</p></li>
@@ -973,14 +970,11 @@ <h1>Block Recurrent Neural Networks<a class="headerlink" href="#block-recurrent-
 <p>Note that you can also use a custom PyTorch Lightning Trainer for training and prediction with optional
 parameter <code class="docutils literal notranslate"><span class="pre">trainer</span></code> in <a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.fit" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">fit()</span></code></a> and <a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.predict" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.predict"><code class="xref py py-func docutils literal notranslate"><span class="pre">predict()</span></code></a>.</p>
 </p></li>
+<li><p><strong>show_warnings</strong> – whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of
+your forecasting use case. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
 </ul>
 </dd>
 </dl>
-<dl class="simple">
-<dt>show_warnings</dt><dd><p>whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of
-your forecasting use case. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
-</dd>
-</dl>
 <p class="rubric">References</p>
 <dl class="citation">
 <dt class="label" id="r1b5ea54ad6d4-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
@@ -1032,9 +1026,15 @@ <h1>Block Recurrent Neural Networks<a class="headerlink" href="#block-recurrent-
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.extreme_lags" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.extreme_lags"><code class="xref py py-obj docutils literal notranslate"><span class="pre">extreme_lags</span></code></a></p></td>
 <td><p>A 6-tuple containing in order: (min target lag, max target lag, min past covariate lag, max past covariate lag, min future covariate lag, max future covariate lag).</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.min_train_samples" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.min_train_samples"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_train_samples</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.first_prediction_index" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.first_prediction_index"><code class="xref py py-obj docutils literal notranslate"><span class="pre">first_prediction_index</span></code></a></p></td>
+<td><p>Returns the index of the first predicted within the output of self.model.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.min_train_samples" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.min_train_samples"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_train_samples</span></code></a></p></td>
 <td><p>The minimum number of samples for training the model.</p></td>
 </tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.min_train_series_length" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.min_train_series_length"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_train_series_length</span></code></a></p></td>
+<td><p>Class property defining the minimum required length for the training series; overriding the default value of 3 of ForecastingModel</p></td>
+</tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.output_chunk_length" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.output_chunk_length"><code class="xref py py-obj docutils literal notranslate"><span class="pre">output_chunk_length</span></code></a></p></td>
 <td><p>Number of time steps predicted at once by the model, not defined for statistical models.</p></td>
 </tr>
@@ -1053,13 +1053,16 @@ <h1>Block Recurrent Neural Networks<a class="headerlink" href="#block-recurrent-
 <tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.supports_static_covariates" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.uses_future_covariates" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.supports_transferrable_series_prediction" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.uses_future_covariates" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.uses_past_covariates" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.uses_past_covariates" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.uses_static_covariates" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.uses_static_covariates" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -1118,39 +1121,45 @@ <h1>Block Recurrent Neural Networks<a class="headerlink" href="#block-recurrent-
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.historical_forecasts" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.historical_forecasts"><code class="xref py py-obj docutils literal notranslate"><span class="pre">historical_forecasts</span></code></a>(series[, ...])</p></td>
 <td><p>Compute the historical forecasts that would have been obtained by this model on (potentially multiple) <cite>series</cite>.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.load" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path, **kwargs)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.initialize_encoders" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.initialize_encoders"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initialize_encoders</span></code></a>()</p></td>
+<td><p>instantiates the SequentialEncoder object based on self._model_encoder_settings and parameter <code class="docutils literal notranslate"><span class="pre">add_encoders</span></code> used at model creation</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.load" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path, **kwargs)</p></td>
 <td><p>Loads a model from a given file path.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.load_from_checkpoint" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.load_from_checkpoint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_from_checkpoint</span></code></a>(model_name[, work_dir, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.load_from_checkpoint" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.load_from_checkpoint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_from_checkpoint</span></code></a>(model_name[, work_dir, ...])</p></td>
 <td><p>Load the model from automatically saved checkpoints under '{work_dir}/darts_logs/{model_name}/checkpoints/'.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.load_weights" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.load_weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_weights</span></code></a>(path[, load_encoders, skip_checks])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.load_weights" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.load_weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_weights</span></code></a>(path[, load_encoders, skip_checks])</p></td>
 <td><p>Loads the weights from a manually saved model (saved with <code class="xref py py-meth docutils literal notranslate"><span class="pre">save()</span></code>).</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.load_weights_from_checkpoint" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.load_weights_from_checkpoint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_weights_from_checkpoint</span></code></a>([model_name, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.load_weights_from_checkpoint" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.load_weights_from_checkpoint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_weights_from_checkpoint</span></code></a>([model_name, ...])</p></td>
 <td><p>Load only the weights from automatically saved checkpoints under '{work_dir}/darts_logs/{model_name}/ checkpoints/'.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.lr_find" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.lr_find"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lr_find</span></code></a>(series[, past_covariates, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.lr_find" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.lr_find"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lr_find</span></code></a>(series[, past_covariates, ...])</p></td>
 <td><p>A wrapper around PyTorch Lightning's <cite>Tuner.lr_find()</cite>.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.predict" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, series, past_covariates, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.predict" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, series, past_covariates, ...])</p></td>
 <td><p>Predict the <code class="docutils literal notranslate"><span class="pre">n</span></code> time step following the end of the training series, or of the specified <code class="docutils literal notranslate"><span class="pre">series</span></code>.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.predict_from_dataset" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.predict_from_dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_from_dataset</span></code></a>(n, input_series_dataset)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.predict_from_dataset" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.predict_from_dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_from_dataset</span></code></a>(n, input_series_dataset)</p></td>
 <td><p>This method allows for predicting with a specific <code class="xref py py-class docutils literal notranslate"><span class="pre">darts.utils.data.InferenceDataset</span></code> instance.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.reset_model" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.reset_model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_model</span></code></a>()</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.reset_model" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.reset_model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_model</span></code></a>()</p></td>
 <td><p>Resets the model object and removes all stored data - model, checkpoints, loggers and training history.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.residuals" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">residuals</span></code></a>(series[, past_covariates, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.residuals" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">residuals</span></code></a>(series[, past_covariates, ...])</p></td>
 <td><p>Compute the residuals produced by this model on a (or sequence of) univariate  time series.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.save" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>([path])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.save" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>([path])</p></td>
 <td><p>Saves the model under a given path.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.to_cpu" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.to_cpu"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_cpu</span></code></a>()</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.to_cpu" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.to_cpu"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_cpu</span></code></a>()</p></td>
 <td><p>Updates the PyTorch Lightning Trainer parameters to move the model to CPU the next time <a href="#id2"><span class="problematic" id="id3">:fun:`fit()`</span></a> or <a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.predict" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.predict"><code class="xref py py-func docutils literal notranslate"><span class="pre">predict()</span></code></a> is called.</p></td>
 </tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.untrained_model" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.untrained_model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">untrained_model</span></code></a>()</p></td>
+<td><p>Returns a new (untrained) model instance create with the same parameters.</p></td>
+</tr>
 </tbody>
 </table>
 <dl class="py method">
@@ -1273,16 +1282,6 @@ <h1>Block Recurrent Neural Networks<a class="headerlink" href="#block-recurrent-
 </dl>
 </dd></dl>
 
-<dl class="py property">
-<dt class="sig sig-object py" id="darts.models.forecasting.block_rnn_model.BlockRNNModel.epochs_trained">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">epochs_trained</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><a class="headerlink" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.epochs_trained" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="field-list simple">
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.block_rnn_model.BlockRNNModel.extreme_lags">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">extreme_lags</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.extreme_lags" title="Permalink to this definition">¶</a></dt>
@@ -1333,6 +1332,17 @@ <h1>Block Recurrent Neural Networks<a class="headerlink" href="#block-recurrent-
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.block_rnn_model.BlockRNNModel.first_prediction_index">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">first_prediction_index</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><a class="headerlink" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.first_prediction_index" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the index of the first predicted within the output of self.model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.block_rnn_model.BlockRNNModel.fit">
 <span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">series</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">past_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_series</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_past_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trainer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_samples_per_ts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_loader_workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.fit" title="Permalink to this definition">¶</a></dt>
@@ -1721,22 +1731,14 @@ <h1>Block Recurrent Neural Networks<a class="headerlink" href="#block-recurrent-
 </dl>
 </dd></dl>
 
-<dl class="py property">
-<dt class="sig sig-object py" id="darts.models.forecasting.block_rnn_model.BlockRNNModel.input_chunk_length">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">input_chunk_length</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><a class="headerlink" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.input_chunk_length" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="field-list simple">
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="darts.models.forecasting.block_rnn_model.BlockRNNModel.likelihood">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">likelihood</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="darts.utils.likelihood_models.html#darts.utils.likelihood_models.Likelihood" title="darts.utils.likelihood_models.Likelihood"><span class="pre">darts.utils.likelihood_models.Likelihood</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.likelihood" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="field-list simple">
+<dl class="py method">
+<dt class="sig sig-object py" id="darts.models.forecasting.block_rnn_model.BlockRNNModel.initialize_encoders">
+<span class="sig-name descname"><span class="pre">initialize_encoders</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.initialize_encoders" title="Permalink to this definition">¶</a></dt>
+<dd><p>instantiates the SequentialEncoder object based on self._model_encoder_settings and parameter
+<code class="docutils literal notranslate"><span class="pre">add_encoders</span></code> used at model creation</p>
+<dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<a class="reference internal" href="darts.utils.likelihood_models.html#darts.utils.likelihood_models.Likelihood" title="darts.utils.likelihood_models.Likelihood"><code class="xref py py-class docutils literal notranslate"><span class="pre">Likelihood</span></code></a>]</p>
+<dd class="field-odd"><p><a class="reference internal" href="darts.dataprocessing.encoders.encoders.html#darts.dataprocessing.encoders.encoders.SequentialEncoder" title="darts.dataprocessing.encoders.encoders.SequentialEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">SequentialEncoder</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1982,21 +1984,13 @@ <h1>Block Recurrent Neural Networks<a class="headerlink" href="#block-recurrent-
 </dd></dl>
 
 <dl class="py property">
-<dt class="sig sig-object py" id="darts.models.forecasting.block_rnn_model.BlockRNNModel.model_created">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">model_created</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.model_created" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="field-list simple">
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="darts.models.forecasting.block_rnn_model.BlockRNNModel.model_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">model_params</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">dict</span></em><a class="headerlink" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.model_params" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="field-list simple">
+<dt class="sig sig-object py" id="darts.models.forecasting.block_rnn_model.BlockRNNModel.min_train_series_length">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">min_train_series_length</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><a class="headerlink" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.min_train_series_length" title="Permalink to this definition">¶</a></dt>
+<dd><p>Class property defining the minimum required length for the training series;
+overriding the default value of 3 of ForecastingModel</p>
+<dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -2072,7 +2066,8 @@ <h1>Block Recurrent Neural Networks<a class="headerlink" href="#block-recurrent-
 This allows bayesian approximation by specifying an implicit prior over learned models.</p></li>
 <li><p><strong>predict_likelihood_parameters</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – If set to <cite>True</cite>, the model predict the parameters of its Likelihood parameters instead of the target. Only
 supported for probabilistic models with a likelihood, <cite>num_samples = 1</cite> and <cite>n&lt;=output_chunk_length</cite>.
-Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
+Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -2203,11 +2198,6 @@ <h1>Block Recurrent Neural Networks<a class="headerlink" href="#block-recurrent-
 </dl>
 </dd></dl>
 
-<dl class="py attribute">
-<dt class="sig sig-object py" id="darts.models.forecasting.block_rnn_model.BlockRNNModel.supports_future_covariates">
-<span class="sig-name descname"><span class="pre">supports_future_covariates</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">False</span></em><a class="headerlink" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.supports_future_covariates" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.block_rnn_model.BlockRNNModel.supports_likelihood_parameter_prediction">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_likelihood_parameter_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.supports_likelihood_parameter_prediction" title="Permalink to this definition">¶</a></dt>
@@ -2263,6 +2253,17 @@ <h1>Block Recurrent Neural Networks<a class="headerlink" href="#block-recurrent-
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.block_rnn_model.BlockRNNModel.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.block_rnn_model.BlockRNNModel.to_cpu">
 <span class="sig-name descname"><span class="pre">to_cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.to_cpu" title="Permalink to this definition">¶</a></dt>
@@ -2270,6 +2271,12 @@ <h1>Block Recurrent Neural Networks<a class="headerlink" href="#block-recurrent-
 <a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.predict" title="darts.models.forecasting.block_rnn_model.BlockRNNModel.predict"><code class="xref py py-func docutils literal notranslate"><span class="pre">predict()</span></code></a> is called.</p>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="darts.models.forecasting.block_rnn_model.BlockRNNModel.untrained_model">
+<span class="sig-name descname"><span class="pre">untrained_model</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.untrained_model" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns a new (untrained) model instance create with the same parameters.</p>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.block_rnn_model.BlockRNNModel.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
@@ -2305,6 +2312,526 @@ <h1>Block Recurrent Neural Networks<a class="headerlink" href="#block-recurrent-
 
 </dd></dl>
 
+<dl class="py class">
+<dt class="sig sig-object py" id="darts.models.forecasting.block_rnn_model.CustomBlockRNNModule">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">darts.models.forecasting.block_rnn_model.</span></span><span class="sig-name descname"><span class="pre">CustomBlockRNNModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nr_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers_out_fc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/block_rnn_model.html#CustomBlockRNNModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.block_rnn_model.CustomBlockRNNModule" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="darts.models.forecasting.pl_forecasting_module.html#darts.models.forecasting.pl_forecasting_module.PLPastCovariatesModule" title="darts.models.forecasting.pl_forecasting_module.PLPastCovariatesModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">darts.models.forecasting.pl_forecasting_module.PLPastCovariatesModule</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">abc.ABC</span></code></p>
+<p>This class allows to create custom block RNN modules that can later be used with Darts’
+<a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel" title="darts.models.forecasting.block_rnn_model.BlockRNNModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">BlockRNNModel</span></code></a>. It adds the backbone that is required to be used with Darts’
+<code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> and <a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel" title="darts.models.forecasting.block_rnn_model.BlockRNNModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">BlockRNNModel</span></code></a>.</p>
+<p>To create a new module, subclass from <a class="reference internal" href="#darts.models.forecasting.block_rnn_model.CustomBlockRNNModule" title="darts.models.forecasting.block_rnn_model.CustomBlockRNNModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">CustomBlockRNNModule</span></code></a> and:</p>
+<ul class="simple">
+<li><p>Define the architecture in the module constructor (<cite>__init__()</cite>)</p></li>
+<li><p>Add the <cite>forward()</cite> method and define the logic of your module’s forward pass</p></li>
+<li><p>Use the custom module class when creating a new <a class="reference internal" href="#darts.models.forecasting.block_rnn_model.BlockRNNModel" title="darts.models.forecasting.block_rnn_model.BlockRNNModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">BlockRNNModel</span></code></a> with parameter <cite>model</cite>.</p></li>
+</ul>
+<p>You can use <cite>darts.models.forecasting.block_rnn_model._BlockRNNModule</cite> as an example.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>input_size</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – The dimensionality of the input time series.</p></li>
+<li><p><strong>hidden_dim</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – The number of features in the hidden state <cite>h</cite> of the RNN module.</p></li>
+<li><p><strong>num_layers</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – The number of recurrent layers.</p></li>
+<li><p><strong>target_size</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – The dimensionality of the output time series.</p></li>
+<li><p><strong>nr_params</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – The number of parameters of the likelihood (or 1 if no likelihood is used).</p></li>
+<li><p><strong>num_layers_out_fc</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">List</span></code>]) – A list containing the dimensions of the hidden layers of the fully connected NN.
+This network connects the last hidden layer of the PyTorch RNN module to the output.</p></li>
+<li><p><strong>dropout</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code>) – The fraction of neurons that are dropped in all-but-last RNN layers.</p></li>
+<li><p><strong>**kwargs</strong> – all parameters required for <code class="xref py py-class docutils literal notranslate"><span class="pre">darts.model.forecasting_models.PLForecastingModule</span></code> base class.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Attributes</p>
+<table class="longtable table autosummary">
+<colgroup>
+<col style="width: 10%" />
+<col style="width: 90%" />
+</colgroup>
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">automatic_optimization</span></code></p></td>
+<td><p>If set to <code class="docutils literal notranslate"><span class="pre">False</span></code> you are responsible for calling <code class="docutils literal notranslate"><span class="pre">.backward()</span></code>, <code class="docutils literal notranslate"><span class="pre">.step()</span></code>, <code class="docutils literal notranslate"><span class="pre">.zero_grad()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">current_epoch</span></code></p></td>
+<td><p>The current epoch in the <code class="docutils literal notranslate"><span class="pre">Trainer</span></code>, or 0 if not attached.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">example_input_array</span></code></p></td>
+<td><p>The example input array is a specification of what the module can consume in the <a class="reference internal" href="#darts.models.forecasting.block_rnn_model.CustomBlockRNNModule.forward" title="darts.models.forecasting.block_rnn_model.CustomBlockRNNModule.forward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">forward()</span></code></a> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">global_rank</span></code></p></td>
+<td><p>The index of the current process across all nodes and devices.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">global_step</span></code></p></td>
+<td><p>Total training batches seen across all epochs.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">hparams</span></code></p></td>
+<td><p>The collection of hyperparameters saved with <code class="xref py py-meth docutils literal notranslate"><span class="pre">save_hyperparameters()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">hparams_initial</span></code></p></td>
+<td><p>The collection of hyperparameters saved with <code class="xref py py-meth docutils literal notranslate"><span class="pre">save_hyperparameters()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">local_rank</span></code></p></td>
+<td><p>The index of the current process within a single node.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">logger</span></code></p></td>
+<td><p>Reference to the logger object in the Trainer.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">loggers</span></code></p></td>
+<td><p>Reference to the list of loggers in the Trainer.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_gpu</span></code></p></td>
+<td><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if this model is currently located on a GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">output_chunk_length</span></code></p></td>
+<td><p>Number of time steps predicted at once by the model.</p></td>
+</tr>
+</tbody>
+</table>
+<table class="table">
+<colgroup>
+<col style="width: 64%" />
+<col style="width: 36%" />
+</colgroup>
+<tbody>
+<tr class="row-odd"><td><p><strong>device</strong></p></td>
+<td></td>
+</tr>
+<tr class="row-even"><td><p><strong>dtype</strong></p></td>
+<td></td>
+</tr>
+<tr class="row-odd"><td><p><strong>epochs_trained</strong></p></td>
+<td></td>
+</tr>
+<tr class="row-even"><td><p><strong>fabric</strong></p></td>
+<td></td>
+</tr>
+<tr class="row-odd"><td><p><strong>trainer</strong></p></td>
+<td></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Methods</p>
+<table class="longtable table autosummary">
+<colgroup>
+<col style="width: 10%" />
+<col style="width: 90%" />
+</colgroup>
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Adds a child module to the current module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">all_gather</span></code>(data[, group, sync_grads])</p></td>
+<td><p>Gather tensors or collections of tensors from multiple processes.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">backward</span></code>(loss, *args, **kwargs)</p></td>
+<td><p>Called to perform backward on the loss returned in <code class="xref py py-meth docutils literal notranslate"><span class="pre">training_step()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Returns an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Returns an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">clip_gradients</span></code>(optimizer[, ...])</p></td>
+<td><p>Handles gradient clipping internally.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">configure_callbacks</span></code>()</p></td>
+<td><p>Configure model-specific callbacks.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">configure_gradient_clipping</span></code>(optimizer[, ...])</p></td>
+<td><p>Perform gradient clipping for the optimizer parameters.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">configure_model</span></code>()</p></td>
+<td><p>Hook to create modules in a strategy and precision aware context.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">configure_optimizers</span></code>()</p></td>
+<td><p>configures optimizers and learning rate schedulers for model optimization.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">configure_sharded_model</span></code>()</p></td>
+<td><p>Deprecated.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">configure_torch_metrics</span></code>(torch_metrics)</p></td>
+<td><p>process the torch_metrics parameter.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.nn.Module.cpu()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Moves all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.nn.Module.double()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Sets the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Set the extra representation of the module</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.nn.Module.float()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.block_rnn_model.CustomBlockRNNModule.forward" title="darts.models.forecasting.block_rnn_model.CustomBlockRNNModule.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(x_in)</p></td>
+<td><p>BlockRNN Module forward.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">freeze</span></code>()</p></td>
+<td><p>Freeze all params for inference.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Returns the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throws an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Returns any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Returns the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throws an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Returns the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throws an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.nn.Module.half()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Moves all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_from_checkpoint</span></code>([map_location, ...])</p></td>
+<td><p>Primary way of loading a model from a checkpoint.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copies parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">log</span></code>(name, value[, prog_bar, logger, ...])</p></td>
+<td><p>Log a key, value pair.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">log_dict</span></code>(dictionary[, prog_bar, logger, ...])</p></td>
+<td><p>Log a dictionary of values at once.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">lr_scheduler_step</span></code>(scheduler, metric)</p></td>
+<td><p>Override this method to adjust the default way the <code class="xref py py-class docutils literal notranslate"><span class="pre">Trainer</span></code> calls each scheduler.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">lr_schedulers</span></code>()</p></td>
+<td><p>Returns the learning rate scheduler(s) that are being used during training.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">manual_backward</span></code>(loss, *args, **kwargs)</p></td>
+<td><p>Call this directly from your <code class="xref py py-meth docutils literal notranslate"><span class="pre">training_step()</span></code> when doing optimizations manually.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Returns an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_after_backward</span></code>()</p></td>
+<td><p>Called after <code class="docutils literal notranslate"><span class="pre">loss.backward()</span></code> and before optimizers are stepped.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_after_batch_transfer</span></code>(batch, dataloader_idx)</p></td>
+<td><p>Override to alter or apply batch augmentations to your batch after it is transferred to the device.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_before_backward</span></code>(loss)</p></td>
+<td><p>Called before <code class="docutils literal notranslate"><span class="pre">loss.backward()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_before_batch_transfer</span></code>(batch, dataloader_idx)</p></td>
+<td><p>Override to alter or apply batch augmentations to your batch before it is transferred to the device.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_before_optimizer_step</span></code>(optimizer)</p></td>
+<td><p>Called before <code class="docutils literal notranslate"><span class="pre">optimizer.step()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_before_zero_grad</span></code>(optimizer)</p></td>
+<td><p>Called after <code class="docutils literal notranslate"><span class="pre">training_step()</span></code> and before <code class="docutils literal notranslate"><span class="pre">optimizer.zero_grad()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_fit_end</span></code>()</p></td>
+<td><p>Called at the very end of fit.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_fit_start</span></code>()</p></td>
+<td><p>Called at the very beginning of fit.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_load_checkpoint</span></code>(checkpoint)</p></td>
+<td><p>Called by Lightning to restore your model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_predict_batch_end</span></code>(outputs, batch, batch_idx)</p></td>
+<td><p>Called in the predict loop after the batch.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_predict_batch_start</span></code>(batch, batch_idx[, ...])</p></td>
+<td><p>Called in the predict loop before anything happens for that batch.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_predict_end</span></code>()</p></td>
+<td><p>Called at the end of predicting.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_predict_epoch_end</span></code>()</p></td>
+<td><p>Called at the end of predicting.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_predict_epoch_start</span></code>()</p></td>
+<td><p>Called at the beginning of predicting.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_predict_model_eval</span></code>()</p></td>
+<td><p>Sets the model to eval during the predict loop.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_predict_start</span></code>()</p></td>
+<td><p>Called at the beginning of predicting.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_save_checkpoint</span></code>(checkpoint)</p></td>
+<td><p>Called by Lightning when saving a checkpoint to give you a chance to store anything else you might want to save.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_test_batch_end</span></code>(outputs, batch, batch_idx)</p></td>
+<td><p>Called in the test loop after the batch.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_test_batch_start</span></code>(batch, batch_idx[, ...])</p></td>
+<td><p>Called in the test loop before anything happens for that batch.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_test_end</span></code>()</p></td>
+<td><p>Called at the end of testing.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_test_epoch_end</span></code>()</p></td>
+<td><p>Called in the test loop at the very end of the epoch.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_test_epoch_start</span></code>()</p></td>
+<td><p>Called in the test loop at the very beginning of the epoch.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_test_model_eval</span></code>()</p></td>
+<td><p>Sets the model to eval during the test loop.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_test_model_train</span></code>()</p></td>
+<td><p>Sets the model to train during the test loop.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_test_start</span></code>()</p></td>
+<td><p>Called at the beginning of testing.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_train_batch_end</span></code>(outputs, batch, batch_idx)</p></td>
+<td><p>Called in the training loop after the batch.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_train_batch_start</span></code>(batch, batch_idx)</p></td>
+<td><p>Called in the training loop before anything happens for that batch.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_train_end</span></code>()</p></td>
+<td><p>Called at the end of training before logger experiment is closed.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_train_epoch_end</span></code>()</p></td>
+<td><p>Called in the training loop at the very end of the epoch.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_train_epoch_start</span></code>()</p></td>
+<td><p>Called in the training loop at the very beginning of the epoch.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_train_start</span></code>()</p></td>
+<td><p>Called at the beginning of training after sanity check.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_validation_batch_end</span></code>(outputs, batch, ...)</p></td>
+<td><p>Called in the validation loop after the batch.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_validation_batch_start</span></code>(batch, batch_idx)</p></td>
+<td><p>Called in the validation loop before anything happens for that batch.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_validation_end</span></code>()</p></td>
+<td><p>Called at the end of validation.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_validation_epoch_end</span></code>()</p></td>
+<td><p>Called in the validation loop at the very end of the epoch.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_validation_epoch_start</span></code>()</p></td>
+<td><p>Called in the validation loop at the very beginning of the epoch.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_validation_model_eval</span></code>()</p></td>
+<td><p>Sets the model to eval during the val loop.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_validation_model_train</span></code>()</p></td>
+<td><p>Sets the model to train during the val loop.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_validation_model_zero_grad</span></code>()</p></td>
+<td><p>Called by the training loop to release gradients before entering the validation loop.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_validation_start</span></code>()</p></td>
+<td><p>Called at the beginning of validation.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimizer_step</span></code>(epoch, batch_idx, optimizer)</p></td>
+<td><p>Override this method to adjust the default way the <code class="xref py py-class docutils literal notranslate"><span class="pre">Trainer</span></code> calls the optimizer.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimizer_zero_grad</span></code>(epoch, batch_idx, optimizer)</p></td>
+<td><p>Override this method to change the default behaviour of <code class="docutils literal notranslate"><span class="pre">optimizer.zero_grad()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimizers</span></code>([use_pl_optimizer])</p></td>
+<td><p>Returns the optimizer(s) that are being used during training.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Returns an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_dataloader</span></code>()</p></td>
+<td><p>An iterable or collection of iterables specifying prediction samples.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_step</span></code>(batch, batch_idx[, dataloader_idx])</p></td>
+<td><p>performs the prediction step</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">prepare_data</span></code>()</p></td>
+<td><p>Use this to download and prepare data.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">print</span></code>(*args, **kwargs)</p></td>
+<td><p>Prints only from process 0.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Registers a backward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Adds a buffer to the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Registers a forward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Registers a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Registers a backward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Registers a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Registers a post hook to be run after module's <code class="docutils literal notranslate"><span class="pre">load_state_dict</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Adds a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>These hooks will be called with arguments: <code class="docutils literal notranslate"><span class="pre">self</span></code>, <code class="docutils literal notranslate"><span class="pre">prefix</span></code>, and <code class="docutils literal notranslate"><span class="pre">keep_vars</span></code> before calling <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> on <code class="docutils literal notranslate"><span class="pre">self</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_hyperparameters</span></code>(*args[, ignore, frame, ...])</p></td>
+<td><p>Save arguments to <code class="docutils literal notranslate"><span class="pre">hparams</span></code> attribute.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>This function is called from <code class="xref py py-func docutils literal notranslate"><span class="pre">load_state_dict()</span></code> to handle any extra state found within the <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_predict_parameters</span></code>(n, num_samples, ...)</p></td>
+<td><p>to be set from TorchForecastingModel before calling trainer.predict() and reset at self.on_predict_end()</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">setup</span></code>(stage)</p></td>
+<td><p>Called at the beginning of fit (train + validate), validate, test, or predict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Returns a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">teardown</span></code>(stage)</p></td>
+<td><p>Called at the end of fit (train + validate), validate, test, or predict.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">test_dataloader</span></code>()</p></td>
+<td><p>An iterable or collection of iterables specifying test samples.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">test_step</span></code>(*args, **kwargs)</p></td>
+<td><p>Operates on a single batch of data from the test set.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.nn.Module.to()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_dtype</span></code>(dtype)</p></td>
+<td><p>Cast module precision (float32 by default) to another precision.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Moves the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_onnx</span></code>(file_path[, input_sample])</p></td>
+<td><p>Saves the model in ONNX format.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_torchscript</span></code>([file_path, method, ...])</p></td>
+<td><p>By default compiles the whole model to a <code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">toggle_optimizer</span></code>(optimizer)</p></td>
+<td><p>Makes sure only the gradients of the current optimizer's parameters are calculated in the training step to prevent dangling gradients in multiple-optimizer setup.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Sets the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train_dataloader</span></code>()</p></td>
+<td><p>An iterable or collection of iterables specifying training samples.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training_step</span></code>(train_batch, batch_idx)</p></td>
+<td><p>performs the training step</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transfer_batch_to_device</span></code>(batch, device, ...)</p></td>
+<td><p>Override this hook if your <code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code> returns tensors wrapped in a custom data structure.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.nn.Module.type()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">unfreeze</span></code>()</p></td>
+<td><p>Unfreeze all parameters for training.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">untoggle_optimizer</span></code>(optimizer)</p></td>
+<td><p>Resets the state of required gradients that were toggled with <code class="xref py py-meth docutils literal notranslate"><span class="pre">toggle_optimizer()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">val_dataloader</span></code>()</p></td>
+<td><p>An iterable or collection of iterables specifying validation samples.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">validation_step</span></code>(val_batch, batch_idx)</p></td>
+<td><p>performs the validation step</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Moves all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Resets gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+<table class="table">
+<colgroup>
+<col style="width: 64%" />
+<col style="width: 36%" />
+</colgroup>
+<tbody>
+<tr class="row-odd"><td><p><strong>__call__</strong></p></td>
+<td></td>
+</tr>
+<tr class="row-even"><td><p><strong>set_mc_dropout</strong></p></td>
+<td></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="darts.models.forecasting.block_rnn_model.CustomBlockRNNModule.forward">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x_in</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/block_rnn_model.html#CustomBlockRNNModule.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.block_rnn_model.CustomBlockRNNModule.forward" title="Permalink to this definition">¶</a></dt>
+<dd><p>BlockRNN Module forward.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>x_in</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Tuple</span></code>) – Tuple of Tensors containing the features of the input sequence. The tuple has elements
+(past target, historic future covariates, future covariates, static covariates).
+The shape of the past target is <cite>(batch_size, input_length, input_size)</cite>.</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>The BlockRNN output Tensor with shape <cite>(batch_size, output_chunk_length, target_size, nr_params)</cite>.
+It contains the prediction at the last time step of the sequence.</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p>torch.Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </section>
 
 
diff --git a/generated_api/darts.models.forecasting.catboost_model.html b/generated_api/darts.models.forecasting.catboost_model.html
index e1b85d9c43..de69e5a720 100644
--- a/generated_api/darts.models.forecasting.catboost_model.html
+++ b/generated_api/darts.models.forecasting.catboost_model.html
@@ -969,13 +969,16 @@ <h1>CatBoost model<a class="headerlink" href="#catboost-model" title="Permalink
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.catboost_model.CatBoostModel.supports_static_covariates" title="darts.models.forecasting.catboost_model.CatBoostModel.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.catboost_model.CatBoostModel.uses_future_covariates" title="darts.models.forecasting.catboost_model.CatBoostModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.catboost_model.CatBoostModel.supports_transferrable_series_prediction" title="darts.models.forecasting.catboost_model.CatBoostModel.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.catboost_model.CatBoostModel.uses_future_covariates" title="darts.models.forecasting.catboost_model.CatBoostModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.catboost_model.CatBoostModel.uses_past_covariates" title="darts.models.forecasting.catboost_model.CatBoostModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.catboost_model.CatBoostModel.uses_past_covariates" title="darts.models.forecasting.catboost_model.CatBoostModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.catboost_model.CatBoostModel.uses_static_covariates" title="darts.models.forecasting.catboost_model.CatBoostModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.catboost_model.CatBoostModel.uses_static_covariates" title="darts.models.forecasting.catboost_model.CatBoostModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -1649,6 +1652,7 @@ <h1>CatBoost model<a class="headerlink" href="#catboost-model" title="Permalink
 Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
 <li><p><strong>**kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Additional keyword arguments passed to the <cite>predict</cite> method of the model. Only works with
 univariate target series.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Return type</dt>
@@ -1788,6 +1792,17 @@ <h1>CatBoost model<a class="headerlink" href="#catboost-model" title="Permalink
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.catboost_model.CatBoostModel.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.catboost_model.CatBoostModel.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.catboost_model.CatBoostModel.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.catboost_model.CatBoostModel.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.croston.html b/generated_api/darts.models.forecasting.croston.html
index 1d7c4b8891..3ca29928e5 100644
--- a/generated_api/darts.models.forecasting.croston.html
+++ b/generated_api/darts.models.forecasting.croston.html
@@ -948,13 +948,16 @@ <h1>Croston method<a class="headerlink" href="#croston-method" title="Permalink
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.croston.Croston.supports_static_covariates" title="darts.models.forecasting.croston.Croston.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.croston.Croston.uses_future_covariates" title="darts.models.forecasting.croston.Croston.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.croston.Croston.supports_transferrable_series_prediction" title="darts.models.forecasting.croston.Croston.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.croston.Croston.uses_future_covariates" title="darts.models.forecasting.croston.Croston.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.croston.Croston.uses_past_covariates" title="darts.models.forecasting.croston.Croston.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.croston.Croston.uses_past_covariates" title="darts.models.forecasting.croston.Croston.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.croston.Croston.uses_static_covariates" title="darts.models.forecasting.croston.Croston.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.croston.Croston.uses_static_covariates" title="darts.models.forecasting.croston.Croston.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -1001,7 +1004,7 @@ <h1>Croston method<a class="headerlink" href="#croston-method" title="Permalink
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.croston.Croston.load" title="darts.models.forecasting.croston.Croston.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path)</p></td>
 <td><p>Loads the model from a given path or file handle.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.croston.Croston.predict" title="darts.models.forecasting.croston.Croston.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, future_covariates, num_samples])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.croston.Croston.predict" title="darts.models.forecasting.croston.Croston.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, future_covariates, num_samples, ...])</p></td>
 <td><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.croston.Croston.residuals" title="darts.models.forecasting.croston.Croston.residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">residuals</span></code></a>(series[, past_covariates, ...])</p></td>
@@ -1547,7 +1550,7 @@ <h1>Croston method<a class="headerlink" href="#croston-method" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.croston.Croston.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.croston.Croston.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.croston.Croston.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p>
 <p>If some future covariates were specified during the training, they must also be specified here.</p>
 <dl class="field-list simple">
@@ -1559,6 +1562,8 @@ <h1>Croston method<a class="headerlink" href="#croston-method" title="Permalink
 contain at least the next <cite>n</cite> time steps/indices after the end of the training target series.</p></li>
 <li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, set the prediction verbosity. Not effective for all models.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -1700,6 +1705,17 @@ <h1>Croston method<a class="headerlink" href="#croston-method" title="Permalink
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.croston.Croston.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.croston.Croston.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.croston.Croston.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.croston.Croston.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.dlinear.html b/generated_api/darts.models.forecasting.dlinear.html
index f4fe89185f..be4b7cee56 100644
--- a/generated_api/darts.models.forecasting.dlinear.html
+++ b/generated_api/darts.models.forecasting.dlinear.html
@@ -902,7 +902,7 @@ <h1>D-Linear<a class="headerlink" href="#d-linear" title="Permalink to this head
 <code class="docutils literal notranslate"><span class="pre">TimeSeries</span></code> is passed to the <a class="reference internal" href="#darts.models.forecasting.dlinear.DLinearModel.fit" title="darts.models.forecasting.dlinear.DLinearModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">fit()</span></code></a> method). Default: <code class="docutils literal notranslate"><span class="pre">1</span></code>.</p></li>
 <li><p><strong>force_reset</strong> – If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, any previously-existing model with the same name will be reset (all checkpoints will
 be discarded). Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
-<li><p><strong>save_checkpoints</strong> – Whether or not to automatically save the untrained model and checkpoints from training.
+<li><p><strong>save_checkpoints</strong> – Whether to automatically save the untrained model and checkpoints from training.
 To load the model from checkpoint, call <code class="xref py py-func docutils literal notranslate"><span class="pre">MyModelClass.load_from_checkpoint()</span></code>, where
 <code class="xref py py-class docutils literal notranslate"><span class="pre">MyModelClass</span></code> is the <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> class that was used (such as <code class="xref py py-class docutils literal notranslate"><span class="pre">TFTModel</span></code>,
 <code class="xref py py-class docutils literal notranslate"><span class="pre">NBEATSModel</span></code>, etc.). If set to <code class="docutils literal notranslate"><span class="pre">False</span></code>, the model can still be manually saved using
@@ -931,8 +931,8 @@ <h1>D-Linear<a class="headerlink" href="#d-linear" title="Permalink to this head
 <li><p><strong>random_state</strong> – Control the randomness of the weights initialization. Check this
 <a class="reference external" href="https://scikit-learn.org/stable/glossary.html#term-random_state">link</a> for more details.
 Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li>
-<li><p><strong>pl_trainer_kwargs</strong> – <dl class="simple">
-<dt>By default <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> creates a PyTorch Lightning Trainer with several useful presets</dt><dd><p>that performs the training, validation and prediction processes. These presets include automatic
+<li><p><strong>pl_trainer_kwargs</strong> – <p>By default <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> creates a PyTorch Lightning Trainer with several useful presets
+that performs the training, validation and prediction processes. These presets include automatic
 checkpointing, tensorboard logging, setting the torch device and more.
 With <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code> you can add additional kwargs to instantiate the PyTorch Lightning trainer
 object. Check the <a class="reference external" href="https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html">PL Trainer documentation</a> for more information about the
@@ -940,8 +940,6 @@ <h1>D-Linear<a class="headerlink" href="#d-linear" title="Permalink to this head
 Running on GPU(s) is also possible using <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code> by specifying keys <code class="docutils literal notranslate"><span class="pre">&quot;accelerator&quot;,</span>
 <span class="pre">&quot;devices&quot;,</span> <span class="pre">and</span> <span class="pre">&quot;auto_select_gpus&quot;</span></code>. Some examples for setting the devices inside the <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code>
 dict:</p>
-</dd>
-</dl>
 <ul>
 <li><p><code class="docutils literal notranslate"><span class="pre">{&quot;accelerator&quot;:</span> <span class="pre">&quot;cpu&quot;}</span></code> for CPU,</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">{&quot;accelerator&quot;:</span> <span class="pre">&quot;gpu&quot;,</span> <span class="pre">&quot;devices&quot;:</span> <span class="pre">[i]}</span></code> to use only GPU <code class="docutils literal notranslate"><span class="pre">i</span></code> (<code class="docutils literal notranslate"><span class="pre">i</span></code> must be an integer),</p></li>
@@ -972,14 +970,11 @@ <h1>D-Linear<a class="headerlink" href="#d-linear" title="Permalink to this head
 <p>Note that you can also use a custom PyTorch Lightning Trainer for training and prediction with optional
 parameter <code class="docutils literal notranslate"><span class="pre">trainer</span></code> in <a class="reference internal" href="#darts.models.forecasting.dlinear.DLinearModel.fit" title="darts.models.forecasting.dlinear.DLinearModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">fit()</span></code></a> and <a class="reference internal" href="#darts.models.forecasting.dlinear.DLinearModel.predict" title="darts.models.forecasting.dlinear.DLinearModel.predict"><code class="xref py py-func docutils literal notranslate"><span class="pre">predict()</span></code></a>.</p>
 </p></li>
+<li><p><strong>show_warnings</strong> – whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of
+your forecasting use case. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
 </ul>
 </dd>
 </dl>
-<dl class="simple">
-<dt>show_warnings</dt><dd><p>whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of
-your forecasting use case. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
-</dd>
-</dl>
 <p class="rubric">References</p>
 <dl class="citation">
 <dt class="label" id="r844e17822ca3-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
@@ -1062,13 +1057,16 @@ <h1>D-Linear<a class="headerlink" href="#d-linear" title="Permalink to this head
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.dlinear.DLinearModel.supports_static_covariates" title="darts.models.forecasting.dlinear.DLinearModel.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.dlinear.DLinearModel.uses_future_covariates" title="darts.models.forecasting.dlinear.DLinearModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.dlinear.DLinearModel.supports_transferrable_series_prediction" title="darts.models.forecasting.dlinear.DLinearModel.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.dlinear.DLinearModel.uses_future_covariates" title="darts.models.forecasting.dlinear.DLinearModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.dlinear.DLinearModel.uses_past_covariates" title="darts.models.forecasting.dlinear.DLinearModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.dlinear.DLinearModel.uses_past_covariates" title="darts.models.forecasting.dlinear.DLinearModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.dlinear.DLinearModel.uses_static_covariates" title="darts.models.forecasting.dlinear.DLinearModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.dlinear.DLinearModel.uses_static_covariates" title="darts.models.forecasting.dlinear.DLinearModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -2081,7 +2079,8 @@ <h1>D-Linear<a class="headerlink" href="#d-linear" title="Permalink to this head
 This allows bayesian approximation by specifying an implicit prior over learned models.</p></li>
 <li><p><strong>predict_likelihood_parameters</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – If set to <cite>True</cite>, the model predict the parameters of its Likelihood parameters instead of the target. Only
 supported for probabilistic models with a likelihood, <cite>num_samples = 1</cite> and <cite>n&lt;=output_chunk_length</cite>.
-Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
+Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -2278,6 +2277,17 @@ <h1>D-Linear<a class="headerlink" href="#d-linear" title="Permalink to this head
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.dlinear.DLinearModel.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.dlinear.DLinearModel.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.dlinear.DLinearModel.to_cpu">
 <span class="sig-name descname"><span class="pre">to_cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.dlinear.DLinearModel.to_cpu" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.ensemble_model.html b/generated_api/darts.models.forecasting.ensemble_model.html
index a44886aa4b..7c472e7522 100644
--- a/generated_api/darts.models.forecasting.ensemble_model.html
+++ b/generated_api/darts.models.forecasting.ensemble_model.html
@@ -904,13 +904,16 @@
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.ensemble_model.EnsembleModel.supports_static_covariates" title="darts.models.forecasting.ensemble_model.EnsembleModel.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.ensemble_model.EnsembleModel.uses_future_covariates" title="darts.models.forecasting.ensemble_model.EnsembleModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.ensemble_model.EnsembleModel.supports_transferrable_series_prediction" title="darts.models.forecasting.ensemble_model.EnsembleModel.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.ensemble_model.EnsembleModel.uses_future_covariates" title="darts.models.forecasting.ensemble_model.EnsembleModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.ensemble_model.EnsembleModel.uses_past_covariates" title="darts.models.forecasting.ensemble_model.EnsembleModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.ensemble_model.EnsembleModel.uses_past_covariates" title="darts.models.forecasting.ensemble_model.EnsembleModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.ensemble_model.EnsembleModel.uses_static_covariates" title="darts.models.forecasting.ensemble_model.EnsembleModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.ensemble_model.EnsembleModel.uses_static_covariates" title="darts.models.forecasting.ensemble_model.EnsembleModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -1688,6 +1691,17 @@
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.ensemble_model.EnsembleModel.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.ensemble_model.EnsembleModel.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.ensemble_model.EnsembleModel.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.ensemble_model.EnsembleModel.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.exponential_smoothing.html b/generated_api/darts.models.forecasting.exponential_smoothing.html
index 71bfbcfeb7..c19eea7bf6 100644
--- a/generated_api/darts.models.forecasting.exponential_smoothing.html
+++ b/generated_api/darts.models.forecasting.exponential_smoothing.html
@@ -930,13 +930,16 @@ <h1>Exponential Smoothing<a class="headerlink" href="#exponential-smoothing" tit
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.supports_static_covariates" title="darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.uses_future_covariates" title="darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.supports_transferrable_series_prediction" title="darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.uses_future_covariates" title="darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.uses_past_covariates" title="darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.uses_past_covariates" title="darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.uses_static_covariates" title="darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.uses_static_covariates" title="darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -983,7 +986,7 @@ <h1>Exponential Smoothing<a class="headerlink" href="#exponential-smoothing" tit
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.load" title="darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path)</p></td>
 <td><p>Loads the model from a given path or file handle.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.predict" title="darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.predict" title="darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose, show_warnings])</p></td>
 <td><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.residuals" title="darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">residuals</span></code></a>(series[, past_covariates, ...])</p></td>
@@ -1523,14 +1526,16 @@ <h1>Exponential Smoothing<a class="headerlink" href="#exponential-smoothing" tit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/exponential_smoothing.html#ExponentialSmoothing.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/exponential_smoothing.html#ExponentialSmoothing.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>n</strong> – Forecast horizon - the number of time steps after the end of the series for which to produce predictions.</p></li>
-<li><p><strong>num_samples</strong> – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
+<li><p><strong>n</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Forecast horizon - the number of time steps after the end of the series for which to produce predictions.</p></li>
+<li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, set the prediction verbosity. Not effective for all models.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -1672,6 +1677,17 @@ <h1>Exponential Smoothing<a class="headerlink" href="#exponential-smoothing" tit
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.fft.html b/generated_api/darts.models.forecasting.fft.html
index f49031f4d0..1eb1a42eae 100644
--- a/generated_api/darts.models.forecasting.fft.html
+++ b/generated_api/darts.models.forecasting.fft.html
@@ -933,13 +933,16 @@ <h1>Fast Fourier Transform<a class="headerlink" href="#fast-fourier-transform" t
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.fft.FFT.supports_static_covariates" title="darts.models.forecasting.fft.FFT.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.fft.FFT.uses_future_covariates" title="darts.models.forecasting.fft.FFT.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.fft.FFT.supports_transferrable_series_prediction" title="darts.models.forecasting.fft.FFT.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.fft.FFT.uses_future_covariates" title="darts.models.forecasting.fft.FFT.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.fft.FFT.uses_past_covariates" title="darts.models.forecasting.fft.FFT.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.fft.FFT.uses_past_covariates" title="darts.models.forecasting.fft.FFT.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.fft.FFT.uses_static_covariates" title="darts.models.forecasting.fft.FFT.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.fft.FFT.uses_static_covariates" title="darts.models.forecasting.fft.FFT.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -986,7 +989,7 @@ <h1>Fast Fourier Transform<a class="headerlink" href="#fast-fourier-transform" t
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.fft.FFT.load" title="darts.models.forecasting.fft.FFT.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path)</p></td>
 <td><p>Loads the model from a given path or file handle.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.fft.FFT.predict" title="darts.models.forecasting.fft.FFT.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.fft.FFT.predict" title="darts.models.forecasting.fft.FFT.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose, show_warnings])</p></td>
 <td><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.fft.FFT.residuals" title="darts.models.forecasting.fft.FFT.residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">residuals</span></code></a>(series[, past_covariates, ...])</p></td>
@@ -1526,7 +1529,7 @@ <h1>Fast Fourier Transform<a class="headerlink" href="#fast-fourier-transform" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.fft.FFT.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/fft.html#FFT.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.fft.FFT.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/fft.html#FFT.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.fft.FFT.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1534,6 +1537,8 @@ <h1>Fast Fourier Transform<a class="headerlink" href="#fast-fourier-transform" t
 <li><p><strong>n</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Forecast horizon - the number of time steps after the end of the series for which to produce predictions.</p></li>
 <li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, set the prediction verbosity. Not effective for all models.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -1675,6 +1680,17 @@ <h1>Fast Fourier Transform<a class="headerlink" href="#fast-fourier-transform" t
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.fft.FFT.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.fft.FFT.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.fft.FFT.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.fft.FFT.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.kalman_forecaster.html b/generated_api/darts.models.forecasting.kalman_forecaster.html
index 43b32f2a24..87f451d71e 100644
--- a/generated_api/darts.models.forecasting.kalman_forecaster.html
+++ b/generated_api/darts.models.forecasting.kalman_forecaster.html
@@ -949,13 +949,16 @@ <h1>Kalman Filter Forecaster<a class="headerlink" href="#kalman-filter-forecaste
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.kalman_forecaster.KalmanForecaster.supports_static_covariates" title="darts.models.forecasting.kalman_forecaster.KalmanForecaster.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.kalman_forecaster.KalmanForecaster.uses_future_covariates" title="darts.models.forecasting.kalman_forecaster.KalmanForecaster.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.kalman_forecaster.KalmanForecaster.supports_transferrable_series_prediction" title="darts.models.forecasting.kalman_forecaster.KalmanForecaster.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.kalman_forecaster.KalmanForecaster.uses_future_covariates" title="darts.models.forecasting.kalman_forecaster.KalmanForecaster.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.kalman_forecaster.KalmanForecaster.uses_past_covariates" title="darts.models.forecasting.kalman_forecaster.KalmanForecaster.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.kalman_forecaster.KalmanForecaster.uses_past_covariates" title="darts.models.forecasting.kalman_forecaster.KalmanForecaster.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.kalman_forecaster.KalmanForecaster.uses_static_covariates" title="darts.models.forecasting.kalman_forecaster.KalmanForecaster.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.kalman_forecaster.KalmanForecaster.uses_static_covariates" title="darts.models.forecasting.kalman_forecaster.KalmanForecaster.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -1548,7 +1551,7 @@ <h1>Kalman Filter Forecaster<a class="headerlink" href="#kalman-filter-forecaste
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.kalman_forecaster.KalmanForecaster.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">series</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/kalman_forecaster.html#KalmanForecaster.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.kalman_forecaster.KalmanForecaster.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">series</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/kalman_forecaster.html#KalmanForecaster.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.kalman_forecaster.KalmanForecaster.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>If the <cite>series</cite> parameter is not set, forecasts values for <cite>n</cite> time steps after the end of the training
 series. If some future covariates were specified during the training, they must also be specified here.</p>
 <p>If the <cite>series</cite> parameter is set, forecasts values for <cite>n</cite> time steps after the end of the new target
@@ -1567,6 +1570,8 @@ <h1>Kalman Filter Forecaster<a class="headerlink" href="#kalman-filter-forecaste
 </p></li>
 <li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, set the prediction verbosity. Not effective for all models.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -1708,6 +1713,17 @@ <h1>Kalman Filter Forecaster<a class="headerlink" href="#kalman-filter-forecaste
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.kalman_forecaster.KalmanForecaster.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.kalman_forecaster.KalmanForecaster.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.kalman_forecaster.KalmanForecaster.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.kalman_forecaster.KalmanForecaster.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.lgbm.html b/generated_api/darts.models.forecasting.lgbm.html
index 6600186e0d..ab88e0f344 100644
--- a/generated_api/darts.models.forecasting.lgbm.html
+++ b/generated_api/darts.models.forecasting.lgbm.html
@@ -995,13 +995,16 @@ <h1>LightGBM Model<a class="headerlink" href="#lightgbm-model" title="Permalink
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.lgbm.LightGBMModel.supports_static_covariates" title="darts.models.forecasting.lgbm.LightGBMModel.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.lgbm.LightGBMModel.uses_future_covariates" title="darts.models.forecasting.lgbm.LightGBMModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.lgbm.LightGBMModel.supports_transferrable_series_prediction" title="darts.models.forecasting.lgbm.LightGBMModel.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.lgbm.LightGBMModel.uses_future_covariates" title="darts.models.forecasting.lgbm.LightGBMModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.lgbm.LightGBMModel.uses_past_covariates" title="darts.models.forecasting.lgbm.LightGBMModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.lgbm.LightGBMModel.uses_past_covariates" title="darts.models.forecasting.lgbm.LightGBMModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.lgbm.LightGBMModel.uses_static_covariates" title="darts.models.forecasting.lgbm.LightGBMModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.lgbm.LightGBMModel.uses_static_covariates" title="darts.models.forecasting.lgbm.LightGBMModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -1678,6 +1681,7 @@ <h1>LightGBM Model<a class="headerlink" href="#lightgbm-model" title="Permalink
 Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
 <li><p><strong>**kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Additional keyword arguments passed to the <cite>predict</cite> method of the model. Only works with
 univariate target series.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Return type</dt>
@@ -1817,6 +1821,17 @@ <h1>LightGBM Model<a class="headerlink" href="#lightgbm-model" title="Permalink
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.lgbm.LightGBMModel.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.lgbm.LightGBMModel.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.lgbm.LightGBMModel.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.lgbm.LightGBMModel.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.linear_regression_model.html b/generated_api/darts.models.forecasting.linear_regression_model.html
index 87a852c8f5..a76887141b 100644
--- a/generated_api/darts.models.forecasting.linear_regression_model.html
+++ b/generated_api/darts.models.forecasting.linear_regression_model.html
@@ -988,13 +988,16 @@ <h1>Linear Regression model<a class="headerlink" href="#linear-regression-model"
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.linear_regression_model.LinearRegressionModel.supports_static_covariates" title="darts.models.forecasting.linear_regression_model.LinearRegressionModel.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.linear_regression_model.LinearRegressionModel.uses_future_covariates" title="darts.models.forecasting.linear_regression_model.LinearRegressionModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.linear_regression_model.LinearRegressionModel.supports_transferrable_series_prediction" title="darts.models.forecasting.linear_regression_model.LinearRegressionModel.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.linear_regression_model.LinearRegressionModel.uses_future_covariates" title="darts.models.forecasting.linear_regression_model.LinearRegressionModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.linear_regression_model.LinearRegressionModel.uses_past_covariates" title="darts.models.forecasting.linear_regression_model.LinearRegressionModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.linear_regression_model.LinearRegressionModel.uses_past_covariates" title="darts.models.forecasting.linear_regression_model.LinearRegressionModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.linear_regression_model.LinearRegressionModel.uses_static_covariates" title="darts.models.forecasting.linear_regression_model.LinearRegressionModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.linear_regression_model.LinearRegressionModel.uses_static_covariates" title="darts.models.forecasting.linear_regression_model.LinearRegressionModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -1666,6 +1669,7 @@ <h1>Linear Regression model<a class="headerlink" href="#linear-regression-model"
 Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
 <li><p><strong>**kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Additional keyword arguments passed to the <cite>predict</cite> method of the model. Only works with
 univariate target series.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Return type</dt>
@@ -1805,6 +1809,17 @@ <h1>Linear Regression model<a class="headerlink" href="#linear-regression-model"
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.linear_regression_model.LinearRegressionModel.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.linear_regression_model.LinearRegressionModel.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.linear_regression_model.LinearRegressionModel.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.linear_regression_model.LinearRegressionModel.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.nbeats.html b/generated_api/darts.models.forecasting.nbeats.html
index d075355ea3..463ba8c6c4 100644
--- a/generated_api/darts.models.forecasting.nbeats.html
+++ b/generated_api/darts.models.forecasting.nbeats.html
@@ -910,7 +910,7 @@ <h1>N-BEATS<a class="headerlink" href="#n-beats" title="Permalink to this headli
 <code class="docutils literal notranslate"><span class="pre">TimeSeries</span></code> is passed to the <a class="reference internal" href="#darts.models.forecasting.nbeats.NBEATSModel.fit" title="darts.models.forecasting.nbeats.NBEATSModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">fit()</span></code></a> method). Default: <code class="docutils literal notranslate"><span class="pre">1</span></code>.</p></li>
 <li><p><strong>force_reset</strong> – If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, any previously-existing model with the same name will be reset (all checkpoints will
 be discarded). Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
-<li><p><strong>save_checkpoints</strong> – Whether or not to automatically save the untrained model and checkpoints from training.
+<li><p><strong>save_checkpoints</strong> – Whether to automatically save the untrained model and checkpoints from training.
 To load the model from checkpoint, call <code class="xref py py-func docutils literal notranslate"><span class="pre">MyModelClass.load_from_checkpoint()</span></code>, where
 <code class="xref py py-class docutils literal notranslate"><span class="pre">MyModelClass</span></code> is the <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> class that was used (such as <code class="xref py py-class docutils literal notranslate"><span class="pre">TFTModel</span></code>,
 <a class="reference internal" href="#darts.models.forecasting.nbeats.NBEATSModel" title="darts.models.forecasting.nbeats.NBEATSModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">NBEATSModel</span></code></a>, etc.). If set to <code class="docutils literal notranslate"><span class="pre">False</span></code>, the model can still be manually saved using
@@ -939,8 +939,8 @@ <h1>N-BEATS<a class="headerlink" href="#n-beats" title="Permalink to this headli
 <li><p><strong>random_state</strong> – Control the randomness of the weights initialization. Check this
 <a class="reference external" href="https://scikit-learn.org/stable/glossary.html#term-random_state">link</a> for more details.
 Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li>
-<li><p><strong>pl_trainer_kwargs</strong> – <dl class="simple">
-<dt>By default <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> creates a PyTorch Lightning Trainer with several useful presets</dt><dd><p>that performs the training, validation and prediction processes. These presets include automatic
+<li><p><strong>pl_trainer_kwargs</strong> – <p>By default <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> creates a PyTorch Lightning Trainer with several useful presets
+that performs the training, validation and prediction processes. These presets include automatic
 checkpointing, tensorboard logging, setting the torch device and more.
 With <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code> you can add additional kwargs to instantiate the PyTorch Lightning trainer
 object. Check the <a class="reference external" href="https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html">PL Trainer documentation</a> for more information about the
@@ -948,8 +948,6 @@ <h1>N-BEATS<a class="headerlink" href="#n-beats" title="Permalink to this headli
 Running on GPU(s) is also possible using <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code> by specifying keys <code class="docutils literal notranslate"><span class="pre">&quot;accelerator&quot;,</span>
 <span class="pre">&quot;devices&quot;,</span> <span class="pre">and</span> <span class="pre">&quot;auto_select_gpus&quot;</span></code>. Some examples for setting the devices inside the <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code>
 dict:</p>
-</dd>
-</dl>
 <ul>
 <li><p><code class="docutils literal notranslate"><span class="pre">{&quot;accelerator&quot;:</span> <span class="pre">&quot;cpu&quot;}</span></code> for CPU,</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">{&quot;accelerator&quot;:</span> <span class="pre">&quot;gpu&quot;,</span> <span class="pre">&quot;devices&quot;:</span> <span class="pre">[i]}</span></code> to use only GPU <code class="docutils literal notranslate"><span class="pre">i</span></code> (<code class="docutils literal notranslate"><span class="pre">i</span></code> must be an integer),</p></li>
@@ -980,14 +978,11 @@ <h1>N-BEATS<a class="headerlink" href="#n-beats" title="Permalink to this headli
 <p>Note that you can also use a custom PyTorch Lightning Trainer for training and prediction with optional
 parameter <code class="docutils literal notranslate"><span class="pre">trainer</span></code> in <a class="reference internal" href="#darts.models.forecasting.nbeats.NBEATSModel.fit" title="darts.models.forecasting.nbeats.NBEATSModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">fit()</span></code></a> and <a class="reference internal" href="#darts.models.forecasting.nbeats.NBEATSModel.predict" title="darts.models.forecasting.nbeats.NBEATSModel.predict"><code class="xref py py-func docutils literal notranslate"><span class="pre">predict()</span></code></a>.</p>
 </p></li>
+<li><p><strong>show_warnings</strong> – whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of
+your forecasting use case. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
 </ul>
 </dd>
 </dl>
-<dl class="simple">
-<dt>show_warnings</dt><dd><p>whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of
-your forecasting use case. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
-</dd>
-</dl>
 <p class="rubric">References</p>
 <dl class="citation">
 <dt class="label" id="r0add55a72971-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
@@ -1064,13 +1059,16 @@ <h1>N-BEATS<a class="headerlink" href="#n-beats" title="Permalink to this headli
 <tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.nbeats.NBEATSModel.supports_static_covariates" title="darts.models.forecasting.nbeats.NBEATSModel.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.nbeats.NBEATSModel.uses_future_covariates" title="darts.models.forecasting.nbeats.NBEATSModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.nbeats.NBEATSModel.supports_transferrable_series_prediction" title="darts.models.forecasting.nbeats.NBEATSModel.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.nbeats.NBEATSModel.uses_future_covariates" title="darts.models.forecasting.nbeats.NBEATSModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.nbeats.NBEATSModel.uses_past_covariates" title="darts.models.forecasting.nbeats.NBEATSModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.nbeats.NBEATSModel.uses_past_covariates" title="darts.models.forecasting.nbeats.NBEATSModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.nbeats.NBEATSModel.uses_static_covariates" title="darts.models.forecasting.nbeats.NBEATSModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.nbeats.NBEATSModel.uses_static_covariates" title="darts.models.forecasting.nbeats.NBEATSModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -2083,7 +2081,8 @@ <h1>N-BEATS<a class="headerlink" href="#n-beats" title="Permalink to this headli
 This allows bayesian approximation by specifying an implicit prior over learned models.</p></li>
 <li><p><strong>predict_likelihood_parameters</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – If set to <cite>True</cite>, the model predict the parameters of its Likelihood parameters instead of the target. Only
 supported for probabilistic models with a likelihood, <cite>num_samples = 1</cite> and <cite>n&lt;=output_chunk_length</cite>.
-Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
+Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -2274,6 +2273,17 @@ <h1>N-BEATS<a class="headerlink" href="#n-beats" title="Permalink to this headli
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.nbeats.NBEATSModel.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.nbeats.NBEATSModel.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.nbeats.NBEATSModel.to_cpu">
 <span class="sig-name descname"><span class="pre">to_cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.nbeats.NBEATSModel.to_cpu" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.nhits.html b/generated_api/darts.models.forecasting.nhits.html
index 93211ae6d8..bd89e5a43d 100644
--- a/generated_api/darts.models.forecasting.nhits.html
+++ b/generated_api/darts.models.forecasting.nhits.html
@@ -918,7 +918,7 @@ <h1>N-HiTS<a class="headerlink" href="#n-hits" title="Permalink to this headline
 <code class="docutils literal notranslate"><span class="pre">TimeSeries</span></code> is passed to the <a class="reference internal" href="#darts.models.forecasting.nhits.NHiTSModel.fit" title="darts.models.forecasting.nhits.NHiTSModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">fit()</span></code></a> method). Default: <code class="docutils literal notranslate"><span class="pre">1</span></code>.</p></li>
 <li><p><strong>force_reset</strong> – If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, any previously-existing model with the same name will be reset (all checkpoints will
 be discarded). Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
-<li><p><strong>save_checkpoints</strong> – Whether or not to automatically save the untrained model and checkpoints from training.
+<li><p><strong>save_checkpoints</strong> – Whether to automatically save the untrained model and checkpoints from training.
 To load the model from checkpoint, call <code class="xref py py-func docutils literal notranslate"><span class="pre">MyModelClass.load_from_checkpoint()</span></code>, where
 <code class="xref py py-class docutils literal notranslate"><span class="pre">MyModelClass</span></code> is the <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> class that was used (such as <code class="xref py py-class docutils literal notranslate"><span class="pre">TFTModel</span></code>,
 <code class="xref py py-class docutils literal notranslate"><span class="pre">NBEATSModel</span></code>, etc.). If set to <code class="docutils literal notranslate"><span class="pre">False</span></code>, the model can still be manually saved using
@@ -947,8 +947,8 @@ <h1>N-HiTS<a class="headerlink" href="#n-hits" title="Permalink to this headline
 <li><p><strong>random_state</strong> – Control the randomness of the weights initialization. Check this
 <a class="reference external" href="https://scikit-learn.org/stable/glossary.html#term-random_state">link</a> for more details.
 Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li>
-<li><p><strong>pl_trainer_kwargs</strong> – <dl class="simple">
-<dt>By default <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> creates a PyTorch Lightning Trainer with several useful presets</dt><dd><p>that performs the training, validation and prediction processes. These presets include automatic
+<li><p><strong>pl_trainer_kwargs</strong> – <p>By default <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> creates a PyTorch Lightning Trainer with several useful presets
+that performs the training, validation and prediction processes. These presets include automatic
 checkpointing, tensorboard logging, setting the torch device and more.
 With <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code> you can add additional kwargs to instantiate the PyTorch Lightning trainer
 object. Check the <a class="reference external" href="https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html">PL Trainer documentation</a> for more information about the
@@ -956,8 +956,6 @@ <h1>N-HiTS<a class="headerlink" href="#n-hits" title="Permalink to this headline
 Running on GPU(s) is also possible using <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code> by specifying keys <code class="docutils literal notranslate"><span class="pre">&quot;accelerator&quot;,</span>
 <span class="pre">&quot;devices&quot;,</span> <span class="pre">and</span> <span class="pre">&quot;auto_select_gpus&quot;</span></code>. Some examples for setting the devices inside the <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code>
 dict:</p>
-</dd>
-</dl>
 <ul>
 <li><p><code class="docutils literal notranslate"><span class="pre">{&quot;accelerator&quot;:</span> <span class="pre">&quot;cpu&quot;}</span></code> for CPU,</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">{&quot;accelerator&quot;:</span> <span class="pre">&quot;gpu&quot;,</span> <span class="pre">&quot;devices&quot;:</span> <span class="pre">[i]}</span></code> to use only GPU <code class="docutils literal notranslate"><span class="pre">i</span></code> (<code class="docutils literal notranslate"><span class="pre">i</span></code> must be an integer),</p></li>
@@ -988,14 +986,11 @@ <h1>N-HiTS<a class="headerlink" href="#n-hits" title="Permalink to this headline
 <p>Note that you can also use a custom PyTorch Lightning Trainer for training and prediction with optional
 parameter <code class="docutils literal notranslate"><span class="pre">trainer</span></code> in <a class="reference internal" href="#darts.models.forecasting.nhits.NHiTSModel.fit" title="darts.models.forecasting.nhits.NHiTSModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">fit()</span></code></a> and <a class="reference internal" href="#darts.models.forecasting.nhits.NHiTSModel.predict" title="darts.models.forecasting.nhits.NHiTSModel.predict"><code class="xref py py-func docutils literal notranslate"><span class="pre">predict()</span></code></a>.</p>
 </p></li>
+<li><p><strong>show_warnings</strong> – whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of
+your forecasting use case. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
 </ul>
 </dd>
 </dl>
-<dl class="simple">
-<dt>show_warnings</dt><dd><p>whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of
-your forecasting use case. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
-</dd>
-</dl>
 <p class="rubric">References</p>
 <dl class="citation">
 <dt class="label" id="r38b5223a17b6-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
@@ -1067,13 +1062,16 @@ <h1>N-HiTS<a class="headerlink" href="#n-hits" title="Permalink to this headline
 <tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.nhits.NHiTSModel.supports_static_covariates" title="darts.models.forecasting.nhits.NHiTSModel.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.nhits.NHiTSModel.uses_future_covariates" title="darts.models.forecasting.nhits.NHiTSModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.nhits.NHiTSModel.supports_transferrable_series_prediction" title="darts.models.forecasting.nhits.NHiTSModel.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.nhits.NHiTSModel.uses_future_covariates" title="darts.models.forecasting.nhits.NHiTSModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.nhits.NHiTSModel.uses_past_covariates" title="darts.models.forecasting.nhits.NHiTSModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.nhits.NHiTSModel.uses_past_covariates" title="darts.models.forecasting.nhits.NHiTSModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.nhits.NHiTSModel.uses_static_covariates" title="darts.models.forecasting.nhits.NHiTSModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.nhits.NHiTSModel.uses_static_covariates" title="darts.models.forecasting.nhits.NHiTSModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -2086,7 +2084,8 @@ <h1>N-HiTS<a class="headerlink" href="#n-hits" title="Permalink to this headline
 This allows bayesian approximation by specifying an implicit prior over learned models.</p></li>
 <li><p><strong>predict_likelihood_parameters</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – If set to <cite>True</cite>, the model predict the parameters of its Likelihood parameters instead of the target. Only
 supported for probabilistic models with a likelihood, <cite>num_samples = 1</cite> and <cite>n&lt;=output_chunk_length</cite>.
-Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
+Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -2277,6 +2276,17 @@ <h1>N-HiTS<a class="headerlink" href="#n-hits" title="Permalink to this headline
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.nhits.NHiTSModel.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.nhits.NHiTSModel.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.nhits.NHiTSModel.to_cpu">
 <span class="sig-name descname"><span class="pre">to_cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.nhits.NHiTSModel.to_cpu" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.nlinear.html b/generated_api/darts.models.forecasting.nlinear.html
index 79614d2427..86b2307a6b 100644
--- a/generated_api/darts.models.forecasting.nlinear.html
+++ b/generated_api/darts.models.forecasting.nlinear.html
@@ -907,7 +907,7 @@ <h1>N-Linear<a class="headerlink" href="#n-linear" title="Permalink to this head
 <code class="docutils literal notranslate"><span class="pre">TimeSeries</span></code> is passed to the <a class="reference internal" href="#darts.models.forecasting.nlinear.NLinearModel.fit" title="darts.models.forecasting.nlinear.NLinearModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">fit()</span></code></a> method). Default: <code class="docutils literal notranslate"><span class="pre">1</span></code>.</p></li>
 <li><p><strong>force_reset</strong> – If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, any previously-existing model with the same name will be reset (all checkpoints will
 be discarded). Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
-<li><p><strong>save_checkpoints</strong> – Whether or not to automatically save the untrained model and checkpoints from training.
+<li><p><strong>save_checkpoints</strong> – Whether to automatically save the untrained model and checkpoints from training.
 To load the model from checkpoint, call <code class="xref py py-func docutils literal notranslate"><span class="pre">MyModelClass.load_from_checkpoint()</span></code>, where
 <code class="xref py py-class docutils literal notranslate"><span class="pre">MyModelClass</span></code> is the <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> class that was used (such as <code class="xref py py-class docutils literal notranslate"><span class="pre">TFTModel</span></code>,
 <code class="xref py py-class docutils literal notranslate"><span class="pre">NBEATSModel</span></code>, etc.). If set to <code class="docutils literal notranslate"><span class="pre">False</span></code>, the model can still be manually saved using
@@ -1058,13 +1058,16 @@ <h1>N-Linear<a class="headerlink" href="#n-linear" title="Permalink to this head
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.nlinear.NLinearModel.supports_static_covariates" title="darts.models.forecasting.nlinear.NLinearModel.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.nlinear.NLinearModel.uses_future_covariates" title="darts.models.forecasting.nlinear.NLinearModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.nlinear.NLinearModel.supports_transferrable_series_prediction" title="darts.models.forecasting.nlinear.NLinearModel.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.nlinear.NLinearModel.uses_future_covariates" title="darts.models.forecasting.nlinear.NLinearModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.nlinear.NLinearModel.uses_past_covariates" title="darts.models.forecasting.nlinear.NLinearModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.nlinear.NLinearModel.uses_past_covariates" title="darts.models.forecasting.nlinear.NLinearModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.nlinear.NLinearModel.uses_static_covariates" title="darts.models.forecasting.nlinear.NLinearModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.nlinear.NLinearModel.uses_static_covariates" title="darts.models.forecasting.nlinear.NLinearModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -2077,7 +2080,8 @@ <h1>N-Linear<a class="headerlink" href="#n-linear" title="Permalink to this head
 This allows bayesian approximation by specifying an implicit prior over learned models.</p></li>
 <li><p><strong>predict_likelihood_parameters</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – If set to <cite>True</cite>, the model predict the parameters of its Likelihood parameters instead of the target. Only
 supported for probabilistic models with a likelihood, <cite>num_samples = 1</cite> and <cite>n&lt;=output_chunk_length</cite>.
-Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
+Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -2274,6 +2278,17 @@ <h1>N-Linear<a class="headerlink" href="#n-linear" title="Permalink to this head
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.nlinear.NLinearModel.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.nlinear.NLinearModel.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.nlinear.NLinearModel.to_cpu">
 <span class="sig-name descname"><span class="pre">to_cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.nlinear.NLinearModel.to_cpu" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.prophet_model.html b/generated_api/darts.models.forecasting.prophet_model.html
index 2890015b51..e9f50bf5f4 100644
--- a/generated_api/darts.models.forecasting.prophet_model.html
+++ b/generated_api/darts.models.forecasting.prophet_model.html
@@ -985,13 +985,16 @@ <h1>Facebook Prophet<a class="headerlink" href="#facebook-prophet" title="Permal
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.prophet_model.Prophet.supports_static_covariates" title="darts.models.forecasting.prophet_model.Prophet.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.prophet_model.Prophet.uses_future_covariates" title="darts.models.forecasting.prophet_model.Prophet.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.prophet_model.Prophet.supports_transferrable_series_prediction" title="darts.models.forecasting.prophet_model.Prophet.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.prophet_model.Prophet.uses_future_covariates" title="darts.models.forecasting.prophet_model.Prophet.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.prophet_model.Prophet.uses_past_covariates" title="darts.models.forecasting.prophet_model.Prophet.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.prophet_model.Prophet.uses_past_covariates" title="darts.models.forecasting.prophet_model.Prophet.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.prophet_model.Prophet.uses_static_covariates" title="darts.models.forecasting.prophet_model.Prophet.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.prophet_model.Prophet.uses_static_covariates" title="darts.models.forecasting.prophet_model.Prophet.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -1041,7 +1044,7 @@ <h1>Facebook Prophet<a class="headerlink" href="#facebook-prophet" title="Permal
 <tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.prophet_model.Prophet.load" title="darts.models.forecasting.prophet_model.Prophet.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path)</p></td>
 <td><p>Loads the model from a given path or file handle.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.prophet_model.Prophet.predict" title="darts.models.forecasting.prophet_model.Prophet.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, future_covariates, num_samples])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.prophet_model.Prophet.predict" title="darts.models.forecasting.prophet_model.Prophet.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, future_covariates, num_samples, ...])</p></td>
 <td><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.prophet_model.Prophet.predict_raw" title="darts.models.forecasting.prophet_model.Prophet.predict_raw"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_raw</span></code></a>(n[, future_covariates])</p></td>
@@ -1626,7 +1629,7 @@ <h1>Facebook Prophet<a class="headerlink" href="#facebook-prophet" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.prophet_model.Prophet.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.prophet_model.Prophet.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.prophet_model.Prophet.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p>
 <p>If some future covariates were specified during the training, they must also be specified here.</p>
 <dl class="field-list simple">
@@ -1638,6 +1641,8 @@ <h1>Facebook Prophet<a class="headerlink" href="#facebook-prophet" title="Permal
 contain at least the next <cite>n</cite> time steps/indices after the end of the training target series.</p></li>
 <li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, set the prediction verbosity. Not effective for all models.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -1792,6 +1797,17 @@ <h1>Facebook Prophet<a class="headerlink" href="#facebook-prophet" title="Permal
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.prophet_model.Prophet.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.prophet_model.Prophet.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.prophet_model.Prophet.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.prophet_model.Prophet.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.random_forest.html b/generated_api/darts.models.forecasting.random_forest.html
index 0878eb86b1..fcff8d2422 100644
--- a/generated_api/darts.models.forecasting.random_forest.html
+++ b/generated_api/darts.models.forecasting.random_forest.html
@@ -986,13 +986,16 @@ <h1>Random Forest<a class="headerlink" href="#random-forest" title="Permalink to
 <tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.random_forest.RandomForest.supports_static_covariates" title="darts.models.forecasting.random_forest.RandomForest.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.random_forest.RandomForest.uses_future_covariates" title="darts.models.forecasting.random_forest.RandomForest.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.random_forest.RandomForest.supports_transferrable_series_prediction" title="darts.models.forecasting.random_forest.RandomForest.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.random_forest.RandomForest.uses_future_covariates" title="darts.models.forecasting.random_forest.RandomForest.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.random_forest.RandomForest.uses_past_covariates" title="darts.models.forecasting.random_forest.RandomForest.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.random_forest.RandomForest.uses_past_covariates" title="darts.models.forecasting.random_forest.RandomForest.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.random_forest.RandomForest.uses_static_covariates" title="darts.models.forecasting.random_forest.RandomForest.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.random_forest.RandomForest.uses_static_covariates" title="darts.models.forecasting.random_forest.RandomForest.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -1653,6 +1656,7 @@ <h1>Random Forest<a class="headerlink" href="#random-forest" title="Permalink to
 Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
 <li><p><strong>**kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Additional keyword arguments passed to the <cite>predict</cite> method of the model. Only works with
 univariate target series.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Return type</dt>
@@ -1792,6 +1796,17 @@ <h1>Random Forest<a class="headerlink" href="#random-forest" title="Permalink to
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.random_forest.RandomForest.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.random_forest.RandomForest.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.random_forest.RandomForest.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.random_forest.RandomForest.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.regression_ensemble_model.html b/generated_api/darts.models.forecasting.regression_ensemble_model.html
index a5420723ef..0318a70ab1 100644
--- a/generated_api/darts.models.forecasting.regression_ensemble_model.html
+++ b/generated_api/darts.models.forecasting.regression_ensemble_model.html
@@ -956,13 +956,16 @@ <h1>Regression ensemble model<a class="headerlink" href="#regression-ensemble-mo
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.supports_static_covariates" title="darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.uses_future_covariates" title="darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.supports_transferrable_series_prediction" title="darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.uses_future_covariates" title="darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.uses_past_covariates" title="darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.uses_past_covariates" title="darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.uses_static_covariates" title="darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.uses_static_covariates" title="darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -1750,6 +1753,17 @@ <h1>Regression ensemble model<a class="headerlink" href="#regression-ensemble-mo
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.regression_model.html b/generated_api/darts.models.forecasting.regression_model.html
index 9b8de65d57..ea3cb47087 100644
--- a/generated_api/darts.models.forecasting.regression_model.html
+++ b/generated_api/darts.models.forecasting.regression_model.html
@@ -994,13 +994,16 @@ <h1>Regression Model<a class="headerlink" href="#regression-model" title="Permal
 <tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_model.RegressionModel.supports_static_covariates" title="darts.models.forecasting.regression_model.RegressionModel.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_model.RegressionModel.uses_future_covariates" title="darts.models.forecasting.regression_model.RegressionModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_model.RegressionModel.supports_transferrable_series_prediction" title="darts.models.forecasting.regression_model.RegressionModel.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_model.RegressionModel.uses_future_covariates" title="darts.models.forecasting.regression_model.RegressionModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_model.RegressionModel.uses_past_covariates" title="darts.models.forecasting.regression_model.RegressionModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_model.RegressionModel.uses_past_covariates" title="darts.models.forecasting.regression_model.RegressionModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_model.RegressionModel.uses_static_covariates" title="darts.models.forecasting.regression_model.RegressionModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_model.RegressionModel.uses_static_covariates" title="darts.models.forecasting.regression_model.RegressionModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -1661,6 +1664,7 @@ <h1>Regression Model<a class="headerlink" href="#regression-model" title="Permal
 Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
 <li><p><strong>**kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Additional keyword arguments passed to the <cite>predict</cite> method of the model. Only works with
 univariate target series.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Return type</dt>
@@ -1800,6 +1804,17 @@ <h1>Regression Model<a class="headerlink" href="#regression-model" title="Permal
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.regression_model.RegressionModel.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.regression_model.RegressionModel.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.regression_model.RegressionModel.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.regression_model.RegressionModel.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
@@ -1937,13 +1952,16 @@ <h1>Regression Model<a class="headerlink" href="#regression-model" title="Permal
 <tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.supports_static_covariates" title="darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.uses_future_covariates" title="darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.supports_transferrable_series_prediction" title="darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.uses_future_covariates" title="darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.uses_past_covariates" title="darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.uses_past_covariates" title="darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.uses_static_covariates" title="darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.uses_static_covariates" title="darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -2604,6 +2622,7 @@ <h1>Regression Model<a class="headerlink" href="#regression-model" title="Permal
 Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
 <li><p><strong>**kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Additional keyword arguments passed to the <cite>predict</cite> method of the model. Only works with
 univariate target series.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Return type</dt>
@@ -2743,6 +2762,17 @@ <h1>Regression Model<a class="headerlink" href="#regression-model" title="Permal
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.rnn_model.html b/generated_api/darts.models.forecasting.rnn_model.html
index 0c321e048e..d425e4df14 100644
--- a/generated_api/darts.models.forecasting.rnn_model.html
+++ b/generated_api/darts.models.forecasting.rnn_model.html
@@ -834,7 +834,7 @@
               
               <div>
                 
-  <span class="target" id="module-darts.models.forecasting.rnn_model"></span><section id="recurrent-neural-networks">
+  <section id="recurrent-neural-networks">
 <h1>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-networks" title="Permalink to this headline">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="darts.models.forecasting.rnn_model.RNNModel">
@@ -864,9 +864,8 @@ <h1>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-netwo
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>input_chunk_length</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of past time steps that are fed to the forecasting module at prediction time.</p></li>
-<li><p><strong>model</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Union</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code>]) – Either a string specifying the RNN module type (“RNN”, “LSTM” or “GRU”),
-or a PyTorch module with the same specifications as
-<cite>darts.models.rnn_model._RNNModule</cite>.</p></li>
+<li><p><strong>model</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Union</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">Type</span></code>[<a class="reference internal" href="#darts.models.forecasting.rnn_model.CustomRNNModule" title="darts.models.forecasting.rnn_model.CustomRNNModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">CustomRNNModule</span></code></a>]]) – Either a string specifying the RNN module type (“RNN”, “LSTM” or “GRU”), or a subclass of
+<a class="reference internal" href="#darts.models.forecasting.rnn_model.CustomRNNModule" title="darts.models.forecasting.rnn_model.CustomRNNModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">CustomRNNModule</span></code></a> (the class itself, not an object of the class) with a custom logic.</p></li>
 <li><p><strong>hidden_dim</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Size for feature maps for each hidden RNN layer (<span class="math notranslate nohighlight">\(h_n\)</span>).</p></li>
 <li><p><strong>n_rnn_layers</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – The number of recurrent layers.</p></li>
 <li><p><strong>dropout</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code>) – Fraction of neurons afected by Dropout.</p></li>
@@ -905,7 +904,7 @@ <h1>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-netwo
 <code class="docutils literal notranslate"><span class="pre">TimeSeries</span></code> is passed to the <a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.fit" title="darts.models.forecasting.rnn_model.RNNModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">fit()</span></code></a> method). Default: <code class="docutils literal notranslate"><span class="pre">1</span></code>.</p></li>
 <li><p><strong>force_reset</strong> – If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, any previously-existing model with the same name will be reset (all checkpoints will
 be discarded). Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
-<li><p><strong>save_checkpoints</strong> – Whether or not to automatically save the untrained model and checkpoints from training.
+<li><p><strong>save_checkpoints</strong> – Whether to automatically save the untrained model and checkpoints from training.
 To load the model from checkpoint, call <code class="xref py py-func docutils literal notranslate"><span class="pre">MyModelClass.load_from_checkpoint()</span></code>, where
 <code class="xref py py-class docutils literal notranslate"><span class="pre">MyModelClass</span></code> is the <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> class that was used (such as <code class="xref py py-class docutils literal notranslate"><span class="pre">TFTModel</span></code>,
 <code class="xref py py-class docutils literal notranslate"><span class="pre">NBEATSModel</span></code>, etc.). If set to <code class="docutils literal notranslate"><span class="pre">False</span></code>, the model can still be manually saved using
@@ -934,8 +933,8 @@ <h1>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-netwo
 <li><p><strong>random_state</strong> – Control the randomness of the weights initialization. Check this
 <a class="reference external" href="https://scikit-learn.org/stable/glossary.html#term-random_state">link</a> for more details.
 Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li>
-<li><p><strong>pl_trainer_kwargs</strong> – <dl class="simple">
-<dt>By default <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> creates a PyTorch Lightning Trainer with several useful presets</dt><dd><p>that performs the training, validation and prediction processes. These presets include automatic
+<li><p><strong>pl_trainer_kwargs</strong> – <p>By default <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> creates a PyTorch Lightning Trainer with several useful presets
+that performs the training, validation and prediction processes. These presets include automatic
 checkpointing, tensorboard logging, setting the torch device and more.
 With <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code> you can add additional kwargs to instantiate the PyTorch Lightning trainer
 object. Check the <a class="reference external" href="https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html">PL Trainer documentation</a> for more information about the
@@ -943,8 +942,6 @@ <h1>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-netwo
 Running on GPU(s) is also possible using <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code> by specifying keys <code class="docutils literal notranslate"><span class="pre">&quot;accelerator&quot;,</span>
 <span class="pre">&quot;devices&quot;,</span> <span class="pre">and</span> <span class="pre">&quot;auto_select_gpus&quot;</span></code>. Some examples for setting the devices inside the <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code>
 dict:</p>
-</dd>
-</dl>
 <ul>
 <li><p><code class="docutils literal notranslate"><span class="pre">{&quot;accelerator&quot;:</span> <span class="pre">&quot;cpu&quot;}</span></code> for CPU,</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">{&quot;accelerator&quot;:</span> <span class="pre">&quot;gpu&quot;,</span> <span class="pre">&quot;devices&quot;:</span> <span class="pre">[i]}</span></code> to use only GPU <code class="docutils literal notranslate"><span class="pre">i</span></code> (<code class="docutils literal notranslate"><span class="pre">i</span></code> must be an integer),</p></li>
@@ -975,14 +972,11 @@ <h1>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-netwo
 <p>Note that you can also use a custom PyTorch Lightning Trainer for training and prediction with optional
 parameter <code class="docutils literal notranslate"><span class="pre">trainer</span></code> in <a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.fit" title="darts.models.forecasting.rnn_model.RNNModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">fit()</span></code></a> and <a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.predict" title="darts.models.forecasting.rnn_model.RNNModel.predict"><code class="xref py py-func docutils literal notranslate"><span class="pre">predict()</span></code></a>.</p>
 </p></li>
+<li><p><strong>show_warnings</strong> – whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of
+your forecasting use case. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
 </ul>
 </dd>
 </dl>
-<dl class="simple">
-<dt>show_warnings</dt><dd><p>whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of
-your forecasting use case. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
-</dd>
-</dl>
 <p class="rubric">Examples</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">darts.datasets</span> <span class="kn">import</span> <span class="n">WeatherDataset</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">darts.models</span> <span class="kn">import</span> <span class="n">RNNModel</span>
@@ -1027,9 +1021,15 @@ <h1>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-netwo
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.extreme_lags" title="darts.models.forecasting.rnn_model.RNNModel.extreme_lags"><code class="xref py py-obj docutils literal notranslate"><span class="pre">extreme_lags</span></code></a></p></td>
 <td><p>A 6-tuple containing in order: (min target lag, max target lag, min past covariate lag, max past covariate lag, min future covariate lag, max future covariate lag).</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.min_train_samples" title="darts.models.forecasting.rnn_model.RNNModel.min_train_samples"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_train_samples</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.first_prediction_index" title="darts.models.forecasting.rnn_model.RNNModel.first_prediction_index"><code class="xref py py-obj docutils literal notranslate"><span class="pre">first_prediction_index</span></code></a></p></td>
+<td><p>Returns the index of the first predicted within the output of self.model.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.min_train_samples" title="darts.models.forecasting.rnn_model.RNNModel.min_train_samples"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_train_samples</span></code></a></p></td>
 <td><p>The minimum number of samples for training the model.</p></td>
 </tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.min_train_series_length" title="darts.models.forecasting.rnn_model.RNNModel.min_train_series_length"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_train_series_length</span></code></a></p></td>
+<td><p>Class property defining the minimum required length for the training series; overriding the default value of 3 of ForecastingModel</p></td>
+</tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.output_chunk_length" title="darts.models.forecasting.rnn_model.RNNModel.output_chunk_length"><code class="xref py py-obj docutils literal notranslate"><span class="pre">output_chunk_length</span></code></a></p></td>
 <td><p>Number of time steps predicted at once by the model, not defined for statistical models.</p></td>
 </tr>
@@ -1048,13 +1048,16 @@ <h1>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-netwo
 <tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.supports_static_covariates" title="darts.models.forecasting.rnn_model.RNNModel.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.uses_future_covariates" title="darts.models.forecasting.rnn_model.RNNModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.supports_transferrable_series_prediction" title="darts.models.forecasting.rnn_model.RNNModel.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.uses_future_covariates" title="darts.models.forecasting.rnn_model.RNNModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.uses_past_covariates" title="darts.models.forecasting.rnn_model.RNNModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.uses_past_covariates" title="darts.models.forecasting.rnn_model.RNNModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.uses_static_covariates" title="darts.models.forecasting.rnn_model.RNNModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.uses_static_covariates" title="darts.models.forecasting.rnn_model.RNNModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -1113,39 +1116,45 @@ <h1>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-netwo
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.historical_forecasts" title="darts.models.forecasting.rnn_model.RNNModel.historical_forecasts"><code class="xref py py-obj docutils literal notranslate"><span class="pre">historical_forecasts</span></code></a>(series[, ...])</p></td>
 <td><p>Compute the historical forecasts that would have been obtained by this model on (potentially multiple) <cite>series</cite>.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.load" title="darts.models.forecasting.rnn_model.RNNModel.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path, **kwargs)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.initialize_encoders" title="darts.models.forecasting.rnn_model.RNNModel.initialize_encoders"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initialize_encoders</span></code></a>()</p></td>
+<td><p>instantiates the SequentialEncoder object based on self._model_encoder_settings and parameter <code class="docutils literal notranslate"><span class="pre">add_encoders</span></code> used at model creation</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.load" title="darts.models.forecasting.rnn_model.RNNModel.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path, **kwargs)</p></td>
 <td><p>Loads a model from a given file path.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.load_from_checkpoint" title="darts.models.forecasting.rnn_model.RNNModel.load_from_checkpoint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_from_checkpoint</span></code></a>(model_name[, work_dir, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.load_from_checkpoint" title="darts.models.forecasting.rnn_model.RNNModel.load_from_checkpoint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_from_checkpoint</span></code></a>(model_name[, work_dir, ...])</p></td>
 <td><p>Load the model from automatically saved checkpoints under '{work_dir}/darts_logs/{model_name}/checkpoints/'.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.load_weights" title="darts.models.forecasting.rnn_model.RNNModel.load_weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_weights</span></code></a>(path[, load_encoders, skip_checks])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.load_weights" title="darts.models.forecasting.rnn_model.RNNModel.load_weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_weights</span></code></a>(path[, load_encoders, skip_checks])</p></td>
 <td><p>Loads the weights from a manually saved model (saved with <code class="xref py py-meth docutils literal notranslate"><span class="pre">save()</span></code>).</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.load_weights_from_checkpoint" title="darts.models.forecasting.rnn_model.RNNModel.load_weights_from_checkpoint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_weights_from_checkpoint</span></code></a>([model_name, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.load_weights_from_checkpoint" title="darts.models.forecasting.rnn_model.RNNModel.load_weights_from_checkpoint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_weights_from_checkpoint</span></code></a>([model_name, ...])</p></td>
 <td><p>Load only the weights from automatically saved checkpoints under '{work_dir}/darts_logs/{model_name}/ checkpoints/'.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.lr_find" title="darts.models.forecasting.rnn_model.RNNModel.lr_find"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lr_find</span></code></a>(series[, past_covariates, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.lr_find" title="darts.models.forecasting.rnn_model.RNNModel.lr_find"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lr_find</span></code></a>(series[, past_covariates, ...])</p></td>
 <td><p>A wrapper around PyTorch Lightning's <cite>Tuner.lr_find()</cite>.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.predict" title="darts.models.forecasting.rnn_model.RNNModel.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, series, past_covariates, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.predict" title="darts.models.forecasting.rnn_model.RNNModel.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, series, past_covariates, ...])</p></td>
 <td><p>Predict the <code class="docutils literal notranslate"><span class="pre">n</span></code> time step following the end of the training series, or of the specified <code class="docutils literal notranslate"><span class="pre">series</span></code>.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.predict_from_dataset" title="darts.models.forecasting.rnn_model.RNNModel.predict_from_dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_from_dataset</span></code></a>(n, input_series_dataset)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.predict_from_dataset" title="darts.models.forecasting.rnn_model.RNNModel.predict_from_dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_from_dataset</span></code></a>(n, input_series_dataset)</p></td>
 <td><p>This method allows for predicting with a specific <code class="xref py py-class docutils literal notranslate"><span class="pre">darts.utils.data.InferenceDataset</span></code> instance.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.reset_model" title="darts.models.forecasting.rnn_model.RNNModel.reset_model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_model</span></code></a>()</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.reset_model" title="darts.models.forecasting.rnn_model.RNNModel.reset_model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_model</span></code></a>()</p></td>
 <td><p>Resets the model object and removes all stored data - model, checkpoints, loggers and training history.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.residuals" title="darts.models.forecasting.rnn_model.RNNModel.residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">residuals</span></code></a>(series[, past_covariates, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.residuals" title="darts.models.forecasting.rnn_model.RNNModel.residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">residuals</span></code></a>(series[, past_covariates, ...])</p></td>
 <td><p>Compute the residuals produced by this model on a (or sequence of) univariate  time series.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.save" title="darts.models.forecasting.rnn_model.RNNModel.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>([path])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.save" title="darts.models.forecasting.rnn_model.RNNModel.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>([path])</p></td>
 <td><p>Saves the model under a given path.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.to_cpu" title="darts.models.forecasting.rnn_model.RNNModel.to_cpu"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_cpu</span></code></a>()</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.to_cpu" title="darts.models.forecasting.rnn_model.RNNModel.to_cpu"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_cpu</span></code></a>()</p></td>
 <td><p>Updates the PyTorch Lightning Trainer parameters to move the model to CPU the next time <a href="#id1"><span class="problematic" id="id2">:fun:`fit()`</span></a> or <a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.predict" title="darts.models.forecasting.rnn_model.RNNModel.predict"><code class="xref py py-func docutils literal notranslate"><span class="pre">predict()</span></code></a> is called.</p></td>
 </tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.untrained_model" title="darts.models.forecasting.rnn_model.RNNModel.untrained_model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">untrained_model</span></code></a>()</p></td>
+<td><p>Returns a new (untrained) model instance create with the same parameters.</p></td>
+</tr>
 </tbody>
 </table>
 <dl class="py method">
@@ -1268,16 +1277,6 @@ <h1>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-netwo
 </dl>
 </dd></dl>
 
-<dl class="py property">
-<dt class="sig sig-object py" id="darts.models.forecasting.rnn_model.RNNModel.epochs_trained">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">epochs_trained</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><a class="headerlink" href="#darts.models.forecasting.rnn_model.RNNModel.epochs_trained" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="field-list simple">
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.rnn_model.RNNModel.extreme_lags">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">extreme_lags</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#darts.models.forecasting.rnn_model.RNNModel.extreme_lags" title="Permalink to this definition">¶</a></dt>
@@ -1328,6 +1327,17 @@ <h1>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-netwo
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.rnn_model.RNNModel.first_prediction_index">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">first_prediction_index</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><a class="headerlink" href="#darts.models.forecasting.rnn_model.RNNModel.first_prediction_index" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the index of the first predicted within the output of self.model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.rnn_model.RNNModel.fit">
 <span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">series</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">past_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_series</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_past_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trainer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_samples_per_ts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_loader_workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.rnn_model.RNNModel.fit" title="Permalink to this definition">¶</a></dt>
@@ -1716,22 +1726,14 @@ <h1>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-netwo
 </dl>
 </dd></dl>
 
-<dl class="py property">
-<dt class="sig sig-object py" id="darts.models.forecasting.rnn_model.RNNModel.input_chunk_length">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">input_chunk_length</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><a class="headerlink" href="#darts.models.forecasting.rnn_model.RNNModel.input_chunk_length" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="field-list simple">
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="darts.models.forecasting.rnn_model.RNNModel.likelihood">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">likelihood</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="darts.utils.likelihood_models.html#darts.utils.likelihood_models.Likelihood" title="darts.utils.likelihood_models.Likelihood"><span class="pre">darts.utils.likelihood_models.Likelihood</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#darts.models.forecasting.rnn_model.RNNModel.likelihood" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="field-list simple">
+<dl class="py method">
+<dt class="sig sig-object py" id="darts.models.forecasting.rnn_model.RNNModel.initialize_encoders">
+<span class="sig-name descname"><span class="pre">initialize_encoders</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.rnn_model.RNNModel.initialize_encoders" title="Permalink to this definition">¶</a></dt>
+<dd><p>instantiates the SequentialEncoder object based on self._model_encoder_settings and parameter
+<code class="docutils literal notranslate"><span class="pre">add_encoders</span></code> used at model creation</p>
+<dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<a class="reference internal" href="darts.utils.likelihood_models.html#darts.utils.likelihood_models.Likelihood" title="darts.utils.likelihood_models.Likelihood"><code class="xref py py-class docutils literal notranslate"><span class="pre">Likelihood</span></code></a>]</p>
+<dd class="field-odd"><p><a class="reference internal" href="darts.dataprocessing.encoders.encoders.html#darts.dataprocessing.encoders.encoders.SequentialEncoder" title="darts.dataprocessing.encoders.encoders.SequentialEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">SequentialEncoder</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1977,21 +1979,13 @@ <h1>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-netwo
 </dd></dl>
 
 <dl class="py property">
-<dt class="sig sig-object py" id="darts.models.forecasting.rnn_model.RNNModel.model_created">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">model_created</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.rnn_model.RNNModel.model_created" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="field-list simple">
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="darts.models.forecasting.rnn_model.RNNModel.model_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">model_params</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">dict</span></em><a class="headerlink" href="#darts.models.forecasting.rnn_model.RNNModel.model_params" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="field-list simple">
+<dt class="sig sig-object py" id="darts.models.forecasting.rnn_model.RNNModel.min_train_series_length">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">min_train_series_length</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><a class="headerlink" href="#darts.models.forecasting.rnn_model.RNNModel.min_train_series_length" title="Permalink to this definition">¶</a></dt>
+<dd><p>Class property defining the minimum required length for the training series;
+overriding the default value of 3 of ForecastingModel</p>
+<dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -2067,7 +2061,8 @@ <h1>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-netwo
 This allows bayesian approximation by specifying an implicit prior over learned models.</p></li>
 <li><p><strong>predict_likelihood_parameters</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – If set to <cite>True</cite>, the model predict the parameters of its Likelihood parameters instead of the target. Only
 supported for probabilistic models with a likelihood, <cite>num_samples = 1</cite> and <cite>n&lt;=output_chunk_length</cite>.
-Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
+Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -2242,11 +2237,6 @@ <h1>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-netwo
 </dl>
 </dd></dl>
 
-<dl class="py attribute">
-<dt class="sig sig-object py" id="darts.models.forecasting.rnn_model.RNNModel.supports_past_covariates">
-<span class="sig-name descname"><span class="pre">supports_past_covariates</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">False</span></em><a class="headerlink" href="#darts.models.forecasting.rnn_model.RNNModel.supports_past_covariates" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.rnn_model.RNNModel.supports_static_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_static_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.rnn_model.RNNModel.supports_static_covariates" title="Permalink to this definition">¶</a></dt>
@@ -2258,6 +2248,17 @@ <h1>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-netwo
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.rnn_model.RNNModel.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.rnn_model.RNNModel.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.rnn_model.RNNModel.to_cpu">
 <span class="sig-name descname"><span class="pre">to_cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.rnn_model.RNNModel.to_cpu" title="Permalink to this definition">¶</a></dt>
@@ -2265,6 +2266,12 @@ <h1>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-netwo
 <a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel.predict" title="darts.models.forecasting.rnn_model.RNNModel.predict"><code class="xref py py-func docutils literal notranslate"><span class="pre">predict()</span></code></a> is called.</p>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="darts.models.forecasting.rnn_model.RNNModel.untrained_model">
+<span class="sig-name descname"><span class="pre">untrained_model</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.rnn_model.RNNModel.untrained_model" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns a new (untrained) model instance create with the same parameters.</p>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.rnn_model.RNNModel.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.rnn_model.RNNModel.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
@@ -2300,6 +2307,529 @@ <h1>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-netwo
 
 </dd></dl>
 
+<dl class="py class">
+<dt class="sig sig-object py" id="darts.models.forecasting.rnn_model.CustomRNNModule">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">darts.models.forecasting.rnn_model.</span></span><span class="sig-name descname"><span class="pre">CustomRNNModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nr_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/rnn_model.html#CustomRNNModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.rnn_model.CustomRNNModule" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="darts.models.forecasting.pl_forecasting_module.html#darts.models.forecasting.pl_forecasting_module.PLDualCovariatesModule" title="darts.models.forecasting.pl_forecasting_module.PLDualCovariatesModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">darts.models.forecasting.pl_forecasting_module.PLDualCovariatesModule</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">abc.ABC</span></code></p>
+<p>This class allows to create custom RNN modules that can later be used with Darts’ <a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel" title="darts.models.forecasting.rnn_model.RNNModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">RNNModel</span></code></a>.
+It adds the backbone that is required to be used with Darts’ <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> and
+<a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel" title="darts.models.forecasting.rnn_model.RNNModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">RNNModel</span></code></a>.</p>
+<p>To create a new module, subclass from <a class="reference internal" href="#darts.models.forecasting.rnn_model.CustomRNNModule" title="darts.models.forecasting.rnn_model.CustomRNNModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">CustomRNNModule</span></code></a> and:</p>
+<ul class="simple">
+<li><p>Define the architecture in the module constructor (<cite>__init__()</cite>)</p></li>
+<li><p>Add the <cite>forward()</cite> method and define the logic of your module’s forward pass</p></li>
+<li><p>Use the custom module class when creating a new <a class="reference internal" href="#darts.models.forecasting.rnn_model.RNNModel" title="darts.models.forecasting.rnn_model.RNNModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">RNNModel</span></code></a> with parameter <cite>model</cite>.</p></li>
+</ul>
+<p>You can use <cite>darts.models.forecasting.rnn_model._RNNModule</cite> as an example.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>input_size</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – The dimensionality of the input time series.</p></li>
+<li><p><strong>hidden_dim</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – The number of features in the hidden state <cite>h</cite> of the RNN module.</p></li>
+<li><p><strong>num_layers</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – The number of recurrent layers.</p></li>
+<li><p><strong>target_size</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – The dimensionality of the output time series.</p></li>
+<li><p><strong>nr_params</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – The number of parameters of the likelihood (or 1 if no likelihood is used).</p></li>
+<li><p><strong>dropout</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code>) – The fraction of neurons that are dropped in all-but-last RNN layers.</p></li>
+<li><p><strong>**kwargs</strong> – all parameters required for <code class="xref py py-class docutils literal notranslate"><span class="pre">darts.model.forecasting_models.PLForecastingModule</span></code> base class.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Attributes</p>
+<table class="longtable table autosummary">
+<colgroup>
+<col style="width: 10%" />
+<col style="width: 90%" />
+</colgroup>
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">automatic_optimization</span></code></p></td>
+<td><p>If set to <code class="docutils literal notranslate"><span class="pre">False</span></code> you are responsible for calling <code class="docutils literal notranslate"><span class="pre">.backward()</span></code>, <code class="docutils literal notranslate"><span class="pre">.step()</span></code>, <code class="docutils literal notranslate"><span class="pre">.zero_grad()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">current_epoch</span></code></p></td>
+<td><p>The current epoch in the <code class="docutils literal notranslate"><span class="pre">Trainer</span></code>, or 0 if not attached.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">example_input_array</span></code></p></td>
+<td><p>The example input array is a specification of what the module can consume in the <a class="reference internal" href="#darts.models.forecasting.rnn_model.CustomRNNModule.forward" title="darts.models.forecasting.rnn_model.CustomRNNModule.forward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">forward()</span></code></a> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">global_rank</span></code></p></td>
+<td><p>The index of the current process across all nodes and devices.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">global_step</span></code></p></td>
+<td><p>Total training batches seen across all epochs.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">hparams</span></code></p></td>
+<td><p>The collection of hyperparameters saved with <code class="xref py py-meth docutils literal notranslate"><span class="pre">save_hyperparameters()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">hparams_initial</span></code></p></td>
+<td><p>The collection of hyperparameters saved with <code class="xref py py-meth docutils literal notranslate"><span class="pre">save_hyperparameters()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">local_rank</span></code></p></td>
+<td><p>The index of the current process within a single node.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">logger</span></code></p></td>
+<td><p>Reference to the logger object in the Trainer.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">loggers</span></code></p></td>
+<td><p>Reference to the list of loggers in the Trainer.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_gpu</span></code></p></td>
+<td><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if this model is currently located on a GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">output_chunk_length</span></code></p></td>
+<td><p>Number of time steps predicted at once by the model.</p></td>
+</tr>
+</tbody>
+</table>
+<table class="table">
+<colgroup>
+<col style="width: 64%" />
+<col style="width: 36%" />
+</colgroup>
+<tbody>
+<tr class="row-odd"><td><p><strong>device</strong></p></td>
+<td></td>
+</tr>
+<tr class="row-even"><td><p><strong>dtype</strong></p></td>
+<td></td>
+</tr>
+<tr class="row-odd"><td><p><strong>epochs_trained</strong></p></td>
+<td></td>
+</tr>
+<tr class="row-even"><td><p><strong>fabric</strong></p></td>
+<td></td>
+</tr>
+<tr class="row-odd"><td><p><strong>trainer</strong></p></td>
+<td></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Methods</p>
+<table class="longtable table autosummary">
+<colgroup>
+<col style="width: 10%" />
+<col style="width: 90%" />
+</colgroup>
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_module</span></code>(name, module)</p></td>
+<td><p>Adds a child module to the current module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">all_gather</span></code>(data[, group, sync_grads])</p></td>
+<td><p>Gather tensors or collections of tensors from multiple processes.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code>(fn)</p></td>
+<td><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">backward</span></code>(loss, *args, **kwargs)</p></td>
+<td><p>Called to perform backward on the loss returned in <code class="xref py py-meth docutils literal notranslate"><span class="pre">training_step()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">bfloat16</span></code>()</p></td>
+<td><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">buffers</span></code>([recurse])</p></td>
+<td><p>Returns an iterator over module buffers.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">children</span></code>()</p></td>
+<td><p>Returns an iterator over immediate children modules.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">clip_gradients</span></code>(optimizer[, ...])</p></td>
+<td><p>Handles gradient clipping internally.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code>(*args, **kwargs)</p></td>
+<td><p>Compile this Module's forward using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">configure_callbacks</span></code>()</p></td>
+<td><p>Configure model-specific callbacks.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">configure_gradient_clipping</span></code>(optimizer[, ...])</p></td>
+<td><p>Perform gradient clipping for the optimizer parameters.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">configure_model</span></code>()</p></td>
+<td><p>Hook to create modules in a strategy and precision aware context.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">configure_optimizers</span></code>()</p></td>
+<td><p>configures optimizers and learning rate schedulers for model optimization.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">configure_sharded_model</span></code>()</p></td>
+<td><p>Deprecated.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">configure_torch_metrics</span></code>(torch_metrics)</p></td>
+<td><p>process the torch_metrics parameter.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.nn.Module.cpu()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cuda</span></code>([device])</p></td>
+<td><p>Moves all model parameters and buffers to the GPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">double</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.nn.Module.double()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval</span></code>()</p></td>
+<td><p>Sets the module in evaluation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">extra_repr</span></code>()</p></td>
+<td><p>Set the extra representation of the module</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">float</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.nn.Module.float()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.rnn_model.CustomRNNModule.forward" title="darts.models.forecasting.rnn_model.CustomRNNModule.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(x_in[, h])</p></td>
+<td><p>RNN Module forward.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">freeze</span></code>()</p></td>
+<td><p>Freeze all params for inference.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_buffer</span></code>(target)</p></td>
+<td><p>Returns the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throws an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_extra_state</span></code>()</p></td>
+<td><p>Returns any extra state to include in the module's state_dict.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_parameter</span></code>(target)</p></td>
+<td><p>Returns the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throws an error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submodule</span></code>(target)</p></td>
+<td><p>Returns the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throws an error.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">half</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.nn.Module.half()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipu</span></code>([device])</p></td>
+<td><p>Moves all model parameters and buffers to the IPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_from_checkpoint</span></code>([map_location, ...])</p></td>
+<td><p>Primary way of loading a model from a checkpoint.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code>(state_dict[, strict, assign])</p></td>
+<td><p>Copies parameters and buffers from <code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code> into this module and its descendants.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">log</span></code>(name, value[, prog_bar, logger, ...])</p></td>
+<td><p>Log a key, value pair.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">log_dict</span></code>(dictionary[, prog_bar, logger, ...])</p></td>
+<td><p>Log a dictionary of values at once.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">lr_scheduler_step</span></code>(scheduler, metric)</p></td>
+<td><p>Override this method to adjust the default way the <code class="xref py py-class docutils literal notranslate"><span class="pre">Trainer</span></code> calls each scheduler.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">lr_schedulers</span></code>()</p></td>
+<td><p>Returns the learning rate scheduler(s) that are being used during training.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">manual_backward</span></code>(loss, *args, **kwargs)</p></td>
+<td><p>Call this directly from your <code class="xref py py-meth docutils literal notranslate"><span class="pre">training_step()</span></code> when doing optimizations manually.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">modules</span></code>()</p></td>
+<td><p>Returns an iterator over all modules in the network.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_buffers</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_children</span></code>()</p></td>
+<td><p>Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_modules</span></code>([memo, prefix, remove_duplicate])</p></td>
+<td><p>Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_parameters</span></code>([prefix, recurse, ...])</p></td>
+<td><p>Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_after_backward</span></code>()</p></td>
+<td><p>Called after <code class="docutils literal notranslate"><span class="pre">loss.backward()</span></code> and before optimizers are stepped.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_after_batch_transfer</span></code>(batch, dataloader_idx)</p></td>
+<td><p>Override to alter or apply batch augmentations to your batch after it is transferred to the device.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_before_backward</span></code>(loss)</p></td>
+<td><p>Called before <code class="docutils literal notranslate"><span class="pre">loss.backward()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_before_batch_transfer</span></code>(batch, dataloader_idx)</p></td>
+<td><p>Override to alter or apply batch augmentations to your batch before it is transferred to the device.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_before_optimizer_step</span></code>(optimizer)</p></td>
+<td><p>Called before <code class="docutils literal notranslate"><span class="pre">optimizer.step()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_before_zero_grad</span></code>(optimizer)</p></td>
+<td><p>Called after <code class="docutils literal notranslate"><span class="pre">training_step()</span></code> and before <code class="docutils literal notranslate"><span class="pre">optimizer.zero_grad()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_fit_end</span></code>()</p></td>
+<td><p>Called at the very end of fit.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_fit_start</span></code>()</p></td>
+<td><p>Called at the very beginning of fit.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_load_checkpoint</span></code>(checkpoint)</p></td>
+<td><p>Called by Lightning to restore your model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_predict_batch_end</span></code>(outputs, batch, batch_idx)</p></td>
+<td><p>Called in the predict loop after the batch.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_predict_batch_start</span></code>(batch, batch_idx[, ...])</p></td>
+<td><p>Called in the predict loop before anything happens for that batch.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_predict_end</span></code>()</p></td>
+<td><p>Called at the end of predicting.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_predict_epoch_end</span></code>()</p></td>
+<td><p>Called at the end of predicting.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_predict_epoch_start</span></code>()</p></td>
+<td><p>Called at the beginning of predicting.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_predict_model_eval</span></code>()</p></td>
+<td><p>Sets the model to eval during the predict loop.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_predict_start</span></code>()</p></td>
+<td><p>Called at the beginning of predicting.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_save_checkpoint</span></code>(checkpoint)</p></td>
+<td><p>Called by Lightning when saving a checkpoint to give you a chance to store anything else you might want to save.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_test_batch_end</span></code>(outputs, batch, batch_idx)</p></td>
+<td><p>Called in the test loop after the batch.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_test_batch_start</span></code>(batch, batch_idx[, ...])</p></td>
+<td><p>Called in the test loop before anything happens for that batch.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_test_end</span></code>()</p></td>
+<td><p>Called at the end of testing.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_test_epoch_end</span></code>()</p></td>
+<td><p>Called in the test loop at the very end of the epoch.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_test_epoch_start</span></code>()</p></td>
+<td><p>Called in the test loop at the very beginning of the epoch.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_test_model_eval</span></code>()</p></td>
+<td><p>Sets the model to eval during the test loop.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_test_model_train</span></code>()</p></td>
+<td><p>Sets the model to train during the test loop.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_test_start</span></code>()</p></td>
+<td><p>Called at the beginning of testing.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_train_batch_end</span></code>(outputs, batch, batch_idx)</p></td>
+<td><p>Called in the training loop after the batch.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_train_batch_start</span></code>(batch, batch_idx)</p></td>
+<td><p>Called in the training loop before anything happens for that batch.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_train_end</span></code>()</p></td>
+<td><p>Called at the end of training before logger experiment is closed.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_train_epoch_end</span></code>()</p></td>
+<td><p>Called in the training loop at the very end of the epoch.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_train_epoch_start</span></code>()</p></td>
+<td><p>Called in the training loop at the very beginning of the epoch.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_train_start</span></code>()</p></td>
+<td><p>Called at the beginning of training after sanity check.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_validation_batch_end</span></code>(outputs, batch, ...)</p></td>
+<td><p>Called in the validation loop after the batch.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_validation_batch_start</span></code>(batch, batch_idx)</p></td>
+<td><p>Called in the validation loop before anything happens for that batch.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_validation_end</span></code>()</p></td>
+<td><p>Called at the end of validation.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_validation_epoch_end</span></code>()</p></td>
+<td><p>Called in the validation loop at the very end of the epoch.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_validation_epoch_start</span></code>()</p></td>
+<td><p>Called in the validation loop at the very beginning of the epoch.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_validation_model_eval</span></code>()</p></td>
+<td><p>Sets the model to eval during the val loop.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_validation_model_train</span></code>()</p></td>
+<td><p>Sets the model to train during the val loop.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_validation_model_zero_grad</span></code>()</p></td>
+<td><p>Called by the training loop to release gradients before entering the validation loop.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">on_validation_start</span></code>()</p></td>
+<td><p>Called at the beginning of validation.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimizer_step</span></code>(epoch, batch_idx, optimizer)</p></td>
+<td><p>Override this method to adjust the default way the <code class="xref py py-class docutils literal notranslate"><span class="pre">Trainer</span></code> calls the optimizer.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimizer_zero_grad</span></code>(epoch, batch_idx, optimizer)</p></td>
+<td><p>Override this method to change the default behaviour of <code class="docutils literal notranslate"><span class="pre">optimizer.zero_grad()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimizers</span></code>([use_pl_optimizer])</p></td>
+<td><p>Returns the optimizer(s) that are being used during training.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameters</span></code>([recurse])</p></td>
+<td><p>Returns an iterator over module parameters.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_dataloader</span></code>()</p></td>
+<td><p>An iterable or collection of iterables specifying prediction samples.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_step</span></code>(batch, batch_idx[, dataloader_idx])</p></td>
+<td><p>performs the prediction step</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">prepare_data</span></code>()</p></td>
+<td><p>Use this to download and prepare data.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">print</span></code>(*args, **kwargs)</p></td>
+<td><p>Prints only from process 0.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_backward_hook</span></code>(hook)</p></td>
+<td><p>Registers a backward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_buffer</span></code>(name, tensor[, persistent])</p></td>
+<td><p>Adds a buffer to the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_hook</span></code>(hook, *[, prepend, ...])</p></td>
+<td><p>Registers a forward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_forward_pre_hook</span></code>(hook, *[, ...])</p></td>
+<td><p>Registers a forward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Registers a backward hook on the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_full_backward_pre_hook</span></code>(hook[, prepend])</p></td>
+<td><p>Registers a backward pre-hook on the module.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_load_state_dict_post_hook</span></code>(hook)</p></td>
+<td><p>Registers a post hook to be run after module's <code class="docutils literal notranslate"><span class="pre">load_state_dict</span></code> is called.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_module</span></code>(name, module)</p></td>
+<td><p>Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_parameter</span></code>(name, param)</p></td>
+<td><p>Adds a parameter to the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_state_dict_pre_hook</span></code>(hook)</p></td>
+<td><p>These hooks will be called with arguments: <code class="docutils literal notranslate"><span class="pre">self</span></code>, <code class="docutils literal notranslate"><span class="pre">prefix</span></code>, and <code class="docutils literal notranslate"><span class="pre">keep_vars</span></code> before calling <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> on <code class="docutils literal notranslate"><span class="pre">self</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad_</span></code>([requires_grad])</p></td>
+<td><p>Change if autograd should record operations on parameters in this module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_hyperparameters</span></code>(*args[, ignore, frame, ...])</p></td>
+<td><p>Save arguments to <code class="docutils literal notranslate"><span class="pre">hparams</span></code> attribute.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_extra_state</span></code>(state)</p></td>
+<td><p>This function is called from <code class="xref py py-func docutils literal notranslate"><span class="pre">load_state_dict()</span></code> to handle any extra state found within the <cite>state_dict</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_predict_parameters</span></code>(n, num_samples, ...)</p></td>
+<td><p>to be set from TorchForecastingModel before calling trainer.predict() and reset at self.on_predict_end()</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">setup</span></code>(stage)</p></td>
+<td><p>Called at the beginning of fit (train + validate), validate, test, or predict.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">share_memory</span></code>()</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">state_dict</span></code>(*args[, destination, prefix, ...])</p></td>
+<td><p>Returns a dictionary containing references to the whole state of the module.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">teardown</span></code>(stage)</p></td>
+<td><p>Called at the end of fit (train + validate), validate, test, or predict.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">test_dataloader</span></code>()</p></td>
+<td><p>An iterable or collection of iterables specifying test samples.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">test_step</span></code>(*args, **kwargs)</p></td>
+<td><p>Operates on a single batch of data from the test set.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code>(*args, **kwargs)</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.nn.Module.to()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_dtype</span></code>(dtype)</p></td>
+<td><p>Cast module precision (float32 by default) to another precision.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_empty</span></code>(*, device[, recurse])</p></td>
+<td><p>Moves the parameters and buffers to the specified device without copying storage.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_onnx</span></code>(file_path[, input_sample])</p></td>
+<td><p>Saves the model in ONNX format.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_torchscript</span></code>([file_path, method, ...])</p></td>
+<td><p>By default compiles the whole model to a <code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">toggle_optimizer</span></code>(optimizer)</p></td>
+<td><p>Makes sure only the gradients of the current optimizer's parameters are calculated in the training step to prevent dangling gradients in multiple-optimizer setup.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code>([mode])</p></td>
+<td><p>Sets the module in training mode.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">train_dataloader</span></code>()</p></td>
+<td><p>An iterable or collection of iterables specifying training samples.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">training_step</span></code>(train_batch, batch_idx)</p></td>
+<td><p>performs the training step</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transfer_batch_to_device</span></code>(batch, device, ...)</p></td>
+<td><p>Override this hook if your <code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code> returns tensors wrapped in a custom data structure.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code>(dst_type)</p></td>
+<td><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.nn.Module.type()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">unfreeze</span></code>()</p></td>
+<td><p>Unfreeze all parameters for training.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">untoggle_optimizer</span></code>(optimizer)</p></td>
+<td><p>Resets the state of required gradients that were toggled with <code class="xref py py-meth docutils literal notranslate"><span class="pre">toggle_optimizer()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">val_dataloader</span></code>()</p></td>
+<td><p>An iterable or collection of iterables specifying validation samples.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">validation_step</span></code>(val_batch, batch_idx)</p></td>
+<td><p>performs the validation step</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">xpu</span></code>([device])</p></td>
+<td><p>Moves all model parameters and buffers to the XPU.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_grad</span></code>([set_to_none])</p></td>
+<td><p>Resets gradients of all model parameters.</p></td>
+</tr>
+</tbody>
+</table>
+<table class="table">
+<colgroup>
+<col style="width: 64%" />
+<col style="width: 36%" />
+</colgroup>
+<tbody>
+<tr class="row-odd"><td><p><strong>__call__</strong></p></td>
+<td></td>
+</tr>
+<tr class="row-even"><td><p><strong>set_mc_dropout</strong></p></td>
+<td></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="darts.models.forecasting.rnn_model.CustomRNNModule.forward">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x_in</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/rnn_model.html#CustomRNNModule.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.rnn_model.CustomRNNModule.forward" title="Permalink to this definition">¶</a></dt>
+<dd><p>RNN Module forward.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x_in</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Tuple</span></code>) – Tuple of Tensors containing the features of the input sequence. The tuple has elements (past target,
+historic future covariates, future covariates, static covariates). The shape of the past target is
+<cite>(batch_size, input_length, input_size)</cite>.</p></li>
+<li><p><strong>h</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code>]) – Optionally, the hidden state.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>Tuple of Tensors with elements (RNN output, hidden state). The RNN output Tensor has shape
+<cite>(batch_size, output_chunk_length, target_size, nr_params)</cite>. It contains the outputs at every
+time step of the input sequence. During training the whole tensor is used as output, whereas during
+prediction we only use y[:, -1, :]. However, this module always returns the whole Tensor.</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p>Tuple[torch.Tensor, torch.Tensor]</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
 </section>
 
 
diff --git a/generated_api/darts.models.forecasting.sf_auto_arima.html b/generated_api/darts.models.forecasting.sf_auto_arima.html
index dec1be7568..abb9157d15 100644
--- a/generated_api/darts.models.forecasting.sf_auto_arima.html
+++ b/generated_api/darts.models.forecasting.sf_auto_arima.html
@@ -931,13 +931,16 @@ <h1>StatsForecastAutoARIMA<a class="headerlink" href="#statsforecastautoarima" t
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.supports_static_covariates" title="darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.uses_future_covariates" title="darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.supports_transferrable_series_prediction" title="darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.uses_future_covariates" title="darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.uses_past_covariates" title="darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.uses_past_covariates" title="darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.uses_static_covariates" title="darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.uses_static_covariates" title="darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -984,7 +987,7 @@ <h1>StatsForecastAutoARIMA<a class="headerlink" href="#statsforecastautoarima" t
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.load" title="darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path)</p></td>
 <td><p>Loads the model from a given path or file handle.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.predict" title="darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, future_covariates, num_samples])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.predict" title="darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, future_covariates, num_samples, ...])</p></td>
 <td><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.residuals" title="darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">residuals</span></code></a>(series[, past_covariates, ...])</p></td>
@@ -1530,7 +1533,7 @@ <h1>StatsForecastAutoARIMA<a class="headerlink" href="#statsforecastautoarima" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p>
 <p>If some future covariates were specified during the training, they must also be specified here.</p>
 <dl class="field-list simple">
@@ -1542,6 +1545,8 @@ <h1>StatsForecastAutoARIMA<a class="headerlink" href="#statsforecastautoarima" t
 contain at least the next <cite>n</cite> time steps/indices after the end of the training target series.</p></li>
 <li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, set the prediction verbosity. Not effective for all models.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -1683,6 +1688,17 @@ <h1>StatsForecastAutoARIMA<a class="headerlink" href="#statsforecastautoarima" t
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.sf_auto_ces.html b/generated_api/darts.models.forecasting.sf_auto_ces.html
index 9e6c5a975d..33e71d0e08 100644
--- a/generated_api/darts.models.forecasting.sf_auto_ces.html
+++ b/generated_api/darts.models.forecasting.sf_auto_ces.html
@@ -905,13 +905,16 @@ <h1>StatsForecastAutoCES<a class="headerlink" href="#statsforecastautoces" title
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.supports_static_covariates" title="darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.uses_future_covariates" title="darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.supports_transferrable_series_prediction" title="darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.uses_future_covariates" title="darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.uses_past_covariates" title="darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.uses_past_covariates" title="darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.uses_static_covariates" title="darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.uses_static_covariates" title="darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -958,7 +961,7 @@ <h1>StatsForecastAutoCES<a class="headerlink" href="#statsforecastautoces" title
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.load" title="darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path)</p></td>
 <td><p>Loads the model from a given path or file handle.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.predict" title="darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.predict" title="darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose, show_warnings])</p></td>
 <td><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.residuals" title="darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">residuals</span></code></a>(series[, past_covariates, ...])</p></td>
@@ -1498,7 +1501,7 @@ <h1>StatsForecastAutoCES<a class="headerlink" href="#statsforecastautoces" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/sf_auto_ces.html#StatsForecastAutoCES.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/sf_auto_ces.html#StatsForecastAutoCES.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1506,6 +1509,8 @@ <h1>StatsForecastAutoCES<a class="headerlink" href="#statsforecastautoces" title
 <li><p><strong>n</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Forecast horizon - the number of time steps after the end of the series for which to produce predictions.</p></li>
 <li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, set the prediction verbosity. Not effective for all models.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -1647,6 +1652,17 @@ <h1>StatsForecastAutoCES<a class="headerlink" href="#statsforecastautoces" title
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.sf_auto_ets.html b/generated_api/darts.models.forecasting.sf_auto_ets.html
index 3bec7b521b..a3d80aa229 100644
--- a/generated_api/darts.models.forecasting.sf_auto_ets.html
+++ b/generated_api/darts.models.forecasting.sf_auto_ets.html
@@ -934,13 +934,16 @@ <h1>StatsForecastAutoETS<a class="headerlink" href="#statsforecastautoets" title
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.supports_static_covariates" title="darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.uses_future_covariates" title="darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.supports_transferrable_series_prediction" title="darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.uses_future_covariates" title="darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.uses_past_covariates" title="darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.uses_past_covariates" title="darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.uses_static_covariates" title="darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.uses_static_covariates" title="darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -987,7 +990,7 @@ <h1>StatsForecastAutoETS<a class="headerlink" href="#statsforecastautoets" title
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.load" title="darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path)</p></td>
 <td><p>Loads the model from a given path or file handle.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.predict" title="darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, future_covariates, num_samples])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.predict" title="darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, future_covariates, num_samples, ...])</p></td>
 <td><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.residuals" title="darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">residuals</span></code></a>(series[, past_covariates, ...])</p></td>
@@ -1533,7 +1536,7 @@ <h1>StatsForecastAutoETS<a class="headerlink" href="#statsforecastautoets" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p>
 <p>If some future covariates were specified during the training, they must also be specified here.</p>
 <dl class="field-list simple">
@@ -1545,6 +1548,8 @@ <h1>StatsForecastAutoETS<a class="headerlink" href="#statsforecastautoets" title
 contain at least the next <cite>n</cite> time steps/indices after the end of the training target series.</p></li>
 <li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, set the prediction verbosity. Not effective for all models.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -1686,6 +1691,17 @@ <h1>StatsForecastAutoETS<a class="headerlink" href="#statsforecastautoets" title
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.sf_auto_theta.html b/generated_api/darts.models.forecasting.sf_auto_theta.html
index ccc816aa94..b4749ce41d 100644
--- a/generated_api/darts.models.forecasting.sf_auto_theta.html
+++ b/generated_api/darts.models.forecasting.sf_auto_theta.html
@@ -907,13 +907,16 @@ <h1>StatsForecastAutoTheta<a class="headerlink" href="#statsforecastautotheta" t
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.supports_static_covariates" title="darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.uses_future_covariates" title="darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.supports_transferrable_series_prediction" title="darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.uses_future_covariates" title="darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.uses_past_covariates" title="darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.uses_past_covariates" title="darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.uses_static_covariates" title="darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.uses_static_covariates" title="darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -960,7 +963,7 @@ <h1>StatsForecastAutoTheta<a class="headerlink" href="#statsforecastautotheta" t
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.load" title="darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path)</p></td>
 <td><p>Loads the model from a given path or file handle.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.predict" title="darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.predict" title="darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose, show_warnings])</p></td>
 <td><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.residuals" title="darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">residuals</span></code></a>(series[, past_covariates, ...])</p></td>
@@ -1500,7 +1503,7 @@ <h1>StatsForecastAutoTheta<a class="headerlink" href="#statsforecastautotheta" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/sf_auto_theta.html#StatsForecastAutoTheta.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/sf_auto_theta.html#StatsForecastAutoTheta.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1508,6 +1511,8 @@ <h1>StatsForecastAutoTheta<a class="headerlink" href="#statsforecastautotheta" t
 <li><p><strong>n</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Forecast horizon - the number of time steps after the end of the series for which to produce predictions.</p></li>
 <li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, set the prediction verbosity. Not effective for all models.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -1649,6 +1654,17 @@ <h1>StatsForecastAutoTheta<a class="headerlink" href="#statsforecastautotheta" t
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.tbats_model.html b/generated_api/darts.models.forecasting.tbats_model.html
index 49568c3b8b..082de070e7 100644
--- a/generated_api/darts.models.forecasting.tbats_model.html
+++ b/generated_api/darts.models.forecasting.tbats_model.html
@@ -946,13 +946,16 @@ <h1>BATS and TBATS<a class="headerlink" href="#bats-and-tbats" title="Permalink
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.BATS.supports_static_covariates" title="darts.models.forecasting.tbats_model.BATS.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.BATS.uses_future_covariates" title="darts.models.forecasting.tbats_model.BATS.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.BATS.supports_transferrable_series_prediction" title="darts.models.forecasting.tbats_model.BATS.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.BATS.uses_future_covariates" title="darts.models.forecasting.tbats_model.BATS.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.BATS.uses_past_covariates" title="darts.models.forecasting.tbats_model.BATS.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.BATS.uses_past_covariates" title="darts.models.forecasting.tbats_model.BATS.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.BATS.uses_static_covariates" title="darts.models.forecasting.tbats_model.BATS.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.BATS.uses_static_covariates" title="darts.models.forecasting.tbats_model.BATS.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -999,7 +1002,7 @@ <h1>BATS and TBATS<a class="headerlink" href="#bats-and-tbats" title="Permalink
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.BATS.load" title="darts.models.forecasting.tbats_model.BATS.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path)</p></td>
 <td><p>Loads the model from a given path or file handle.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.BATS.predict" title="darts.models.forecasting.tbats_model.BATS.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.BATS.predict" title="darts.models.forecasting.tbats_model.BATS.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose, show_warnings])</p></td>
 <td><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.BATS.residuals" title="darts.models.forecasting.tbats_model.BATS.residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">residuals</span></code></a>(series[, past_covariates, ...])</p></td>
@@ -1539,14 +1542,16 @@ <h1>BATS and TBATS<a class="headerlink" href="#bats-and-tbats" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.tbats_model.BATS.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.tbats_model.BATS.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.tbats_model.BATS.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>n</strong> – Forecast horizon - the number of time steps after the end of the series for which to produce predictions.</p></li>
-<li><p><strong>num_samples</strong> – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
+<li><p><strong>n</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Forecast horizon - the number of time steps after the end of the series for which to produce predictions.</p></li>
+<li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, set the prediction verbosity. Not effective for all models.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -1688,6 +1693,17 @@ <h1>BATS and TBATS<a class="headerlink" href="#bats-and-tbats" title="Permalink
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.tbats_model.BATS.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.tbats_model.BATS.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.tbats_model.BATS.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.tbats_model.BATS.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
@@ -1817,13 +1833,16 @@ <h1>BATS and TBATS<a class="headerlink" href="#bats-and-tbats" title="Permalink
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.TBATS.supports_static_covariates" title="darts.models.forecasting.tbats_model.TBATS.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.TBATS.uses_future_covariates" title="darts.models.forecasting.tbats_model.TBATS.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.TBATS.supports_transferrable_series_prediction" title="darts.models.forecasting.tbats_model.TBATS.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.TBATS.uses_future_covariates" title="darts.models.forecasting.tbats_model.TBATS.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.TBATS.uses_past_covariates" title="darts.models.forecasting.tbats_model.TBATS.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.TBATS.uses_past_covariates" title="darts.models.forecasting.tbats_model.TBATS.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.TBATS.uses_static_covariates" title="darts.models.forecasting.tbats_model.TBATS.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.TBATS.uses_static_covariates" title="darts.models.forecasting.tbats_model.TBATS.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -1870,7 +1889,7 @@ <h1>BATS and TBATS<a class="headerlink" href="#bats-and-tbats" title="Permalink
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.TBATS.load" title="darts.models.forecasting.tbats_model.TBATS.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path)</p></td>
 <td><p>Loads the model from a given path or file handle.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.TBATS.predict" title="darts.models.forecasting.tbats_model.TBATS.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.TBATS.predict" title="darts.models.forecasting.tbats_model.TBATS.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose, show_warnings])</p></td>
 <td><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tbats_model.TBATS.residuals" title="darts.models.forecasting.tbats_model.TBATS.residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">residuals</span></code></a>(series[, past_covariates, ...])</p></td>
@@ -2410,14 +2429,16 @@ <h1>BATS and TBATS<a class="headerlink" href="#bats-and-tbats" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.tbats_model.TBATS.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.tbats_model.TBATS.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.tbats_model.TBATS.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>n</strong> – Forecast horizon - the number of time steps after the end of the series for which to produce predictions.</p></li>
-<li><p><strong>num_samples</strong> – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
+<li><p><strong>n</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Forecast horizon - the number of time steps after the end of the series for which to produce predictions.</p></li>
+<li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, set the prediction verbosity. Not effective for all models.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -2559,6 +2580,17 @@ <h1>BATS and TBATS<a class="headerlink" href="#bats-and-tbats" title="Permalink
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.tbats_model.TBATS.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.tbats_model.TBATS.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.tbats_model.TBATS.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.tbats_model.TBATS.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.tcn_model.html b/generated_api/darts.models.forecasting.tcn_model.html
index 3b3c81f70c..e735a0f9db 100644
--- a/generated_api/darts.models.forecasting.tcn_model.html
+++ b/generated_api/darts.models.forecasting.tcn_model.html
@@ -894,7 +894,7 @@ <h1>Temporal Convolutional Network<a class="headerlink" href="#temporal-convolut
 <code class="docutils literal notranslate"><span class="pre">TimeSeries</span></code> is passed to the <a class="reference internal" href="#darts.models.forecasting.tcn_model.TCNModel.fit" title="darts.models.forecasting.tcn_model.TCNModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">fit()</span></code></a> method). Default: <code class="docutils literal notranslate"><span class="pre">1</span></code>.</p></li>
 <li><p><strong>force_reset</strong> – If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, any previously-existing model with the same name will be reset (all checkpoints will
 be discarded). Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
-<li><p><strong>save_checkpoints</strong> – Whether or not to automatically save the untrained model and checkpoints from training.
+<li><p><strong>save_checkpoints</strong> – Whether to automatically save the untrained model and checkpoints from training.
 To load the model from checkpoint, call <code class="xref py py-func docutils literal notranslate"><span class="pre">MyModelClass.load_from_checkpoint()</span></code>, where
 <code class="xref py py-class docutils literal notranslate"><span class="pre">MyModelClass</span></code> is the <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> class that was used (such as <code class="xref py py-class docutils literal notranslate"><span class="pre">TFTModel</span></code>,
 <code class="xref py py-class docutils literal notranslate"><span class="pre">NBEATSModel</span></code>, etc.). If set to <code class="docutils literal notranslate"><span class="pre">False</span></code>, the model can still be manually saved using
@@ -923,8 +923,8 @@ <h1>Temporal Convolutional Network<a class="headerlink" href="#temporal-convolut
 <li><p><strong>random_state</strong> – Control the randomness of the weights initialization. Check this
 <a class="reference external" href="https://scikit-learn.org/stable/glossary.html#term-random_state">link</a> for more details.
 Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li>
-<li><p><strong>pl_trainer_kwargs</strong> – <dl class="simple">
-<dt>By default <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> creates a PyTorch Lightning Trainer with several useful presets</dt><dd><p>that performs the training, validation and prediction processes. These presets include automatic
+<li><p><strong>pl_trainer_kwargs</strong> – <p>By default <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> creates a PyTorch Lightning Trainer with several useful presets
+that performs the training, validation and prediction processes. These presets include automatic
 checkpointing, tensorboard logging, setting the torch device and more.
 With <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code> you can add additional kwargs to instantiate the PyTorch Lightning trainer
 object. Check the <a class="reference external" href="https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html">PL Trainer documentation</a> for more information about the
@@ -933,8 +933,6 @@ <h1>Temporal Convolutional Network<a class="headerlink" href="#temporal-convolut
 <span class="pre">&quot;devices&quot;,</span> <span class="pre">and</span> <span class="pre">&quot;auto_select_gpus&quot;</span></code>. Some examples for setting the devices inside the <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code>
 dict:rgs``
 dict:</p>
-</dd>
-</dl>
 <ul>
 <li><p><code class="docutils literal notranslate"><span class="pre">{&quot;accelerator&quot;:</span> <span class="pre">&quot;cpu&quot;}</span></code> for CPU,</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">{&quot;accelerator&quot;:</span> <span class="pre">&quot;gpu&quot;,</span> <span class="pre">&quot;devices&quot;:</span> <span class="pre">[i]}</span></code> to use only GPU <code class="docutils literal notranslate"><span class="pre">i</span></code> (<code class="docutils literal notranslate"><span class="pre">i</span></code> must be an integer),</p></li>
@@ -965,14 +963,11 @@ <h1>Temporal Convolutional Network<a class="headerlink" href="#temporal-convolut
 <p>Note that you can also use a custom PyTorch Lightning Trainer for training and prediction with optional
 parameter <code class="docutils literal notranslate"><span class="pre">trainer</span></code> in <a class="reference internal" href="#darts.models.forecasting.tcn_model.TCNModel.fit" title="darts.models.forecasting.tcn_model.TCNModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">fit()</span></code></a> and <a class="reference internal" href="#darts.models.forecasting.tcn_model.TCNModel.predict" title="darts.models.forecasting.tcn_model.TCNModel.predict"><code class="xref py py-func docutils literal notranslate"><span class="pre">predict()</span></code></a>.</p>
 </p></li>
+<li><p><strong>show_warnings</strong> – whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of
+your forecasting use case. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
 </ul>
 </dd>
 </dl>
-<dl class="simple">
-<dt>show_warnings</dt><dd><p>whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of
-your forecasting use case. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
-</dd>
-</dl>
 <p class="rubric">References</p>
 <dl class="citation">
 <dt class="label" id="r632dab2f2590-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
@@ -1047,13 +1042,16 @@ <h1>Temporal Convolutional Network<a class="headerlink" href="#temporal-convolut
 <tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tcn_model.TCNModel.supports_static_covariates" title="darts.models.forecasting.tcn_model.TCNModel.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tcn_model.TCNModel.uses_future_covariates" title="darts.models.forecasting.tcn_model.TCNModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tcn_model.TCNModel.supports_transferrable_series_prediction" title="darts.models.forecasting.tcn_model.TCNModel.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tcn_model.TCNModel.uses_future_covariates" title="darts.models.forecasting.tcn_model.TCNModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tcn_model.TCNModel.uses_past_covariates" title="darts.models.forecasting.tcn_model.TCNModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tcn_model.TCNModel.uses_past_covariates" title="darts.models.forecasting.tcn_model.TCNModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tcn_model.TCNModel.uses_static_covariates" title="darts.models.forecasting.tcn_model.TCNModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tcn_model.TCNModel.uses_static_covariates" title="darts.models.forecasting.tcn_model.TCNModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -2066,7 +2064,8 @@ <h1>Temporal Convolutional Network<a class="headerlink" href="#temporal-convolut
 This allows bayesian approximation by specifying an implicit prior over learned models.</p></li>
 <li><p><strong>predict_likelihood_parameters</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – If set to <cite>True</cite>, the model predict the parameters of its Likelihood parameters instead of the target. Only
 supported for probabilistic models with a likelihood, <cite>num_samples = 1</cite> and <cite>n&lt;=output_chunk_length</cite>.
-Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
+Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -2257,6 +2256,17 @@ <h1>Temporal Convolutional Network<a class="headerlink" href="#temporal-convolut
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.tcn_model.TCNModel.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.tcn_model.TCNModel.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.tcn_model.TCNModel.to_cpu">
 <span class="sig-name descname"><span class="pre">to_cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.tcn_model.TCNModel.to_cpu" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.tft_model.html b/generated_api/darts.models.forecasting.tft_model.html
index d2b794abe2..c5c685936d 100644
--- a/generated_api/darts.models.forecasting.tft_model.html
+++ b/generated_api/darts.models.forecasting.tft_model.html
@@ -929,7 +929,7 @@ <h1>Temporal Fusion Transformer (TFT)<a class="headerlink" href="#temporal-fusio
 <code class="docutils literal notranslate"><span class="pre">TimeSeries</span></code> is passed to the <a class="reference internal" href="#darts.models.forecasting.tft_model.TFTModel.fit" title="darts.models.forecasting.tft_model.TFTModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">fit()</span></code></a> method). Default: <code class="docutils literal notranslate"><span class="pre">1</span></code>.</p></li>
 <li><p><strong>force_reset</strong> – If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, any previously-existing model with the same name will be reset (all checkpoints will
 be discarded). Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
-<li><p><strong>save_checkpoints</strong> – Whether or not to automatically save the untrained model and checkpoints from training.
+<li><p><strong>save_checkpoints</strong> – Whether to automatically save the untrained model and checkpoints from training.
 To load the model from checkpoint, call <code class="xref py py-func docutils literal notranslate"><span class="pre">MyModelClass.load_from_checkpoint()</span></code>, where
 <code class="xref py py-class docutils literal notranslate"><span class="pre">MyModelClass</span></code> is the <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> class that was used (such as <a class="reference internal" href="#darts.models.forecasting.tft_model.TFTModel" title="darts.models.forecasting.tft_model.TFTModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">TFTModel</span></code></a>,
 <code class="xref py py-class docutils literal notranslate"><span class="pre">NBEATSModel</span></code>, etc.). If set to <code class="docutils literal notranslate"><span class="pre">False</span></code>, the model can still be manually saved using
@@ -958,8 +958,8 @@ <h1>Temporal Fusion Transformer (TFT)<a class="headerlink" href="#temporal-fusio
 <li><p><strong>random_state</strong> – Control the randomness of the weight’s initialization. Check this
 <a class="reference external" href="https://scikit-learn.org/stable/glossary.html#term-random_state">link</a> for more details.
 Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li>
-<li><p><strong>pl_trainer_kwargs</strong> – <dl class="simple">
-<dt>By default <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> creates a PyTorch Lightning Trainer with several useful presets</dt><dd><p>that performs the training, validation and prediction processes. These presets include automatic
+<li><p><strong>pl_trainer_kwargs</strong> – <p>By default <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> creates a PyTorch Lightning Trainer with several useful presets
+that performs the training, validation and prediction processes. These presets include automatic
 checkpointing, tensorboard logging, setting the torch device and more.
 With <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code> you can add additional kwargs to instantiate the PyTorch Lightning trainer
 object. Check the <a class="reference external" href="https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html">PL Trainer documentation</a> for more information about the
@@ -967,8 +967,6 @@ <h1>Temporal Fusion Transformer (TFT)<a class="headerlink" href="#temporal-fusio
 Running on GPU(s) is also possible using <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code> by specifying keys <code class="docutils literal notranslate"><span class="pre">&quot;accelerator&quot;,</span>
 <span class="pre">&quot;devices&quot;,</span> <span class="pre">and</span> <span class="pre">&quot;auto_select_gpus&quot;</span></code>. Some examples for setting the devices inside the <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code>
 dict:</p>
-</dd>
-</dl>
 <ul>
 <li><p><code class="docutils literal notranslate"><span class="pre">{&quot;accelerator&quot;:</span> <span class="pre">&quot;cpu&quot;}</span></code> for CPU,</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">{&quot;accelerator&quot;:</span> <span class="pre">&quot;gpu&quot;,</span> <span class="pre">&quot;devices&quot;:</span> <span class="pre">[i]}</span></code> to use only GPU <code class="docutils literal notranslate"><span class="pre">i</span></code> (<code class="docutils literal notranslate"><span class="pre">i</span></code> must be an integer),</p></li>
@@ -999,14 +997,11 @@ <h1>Temporal Fusion Transformer (TFT)<a class="headerlink" href="#temporal-fusio
 <p>Note that you can also use a custom PyTorch Lightning Trainer for training and prediction with optional
 parameter <code class="docutils literal notranslate"><span class="pre">trainer</span></code> in <a class="reference internal" href="#darts.models.forecasting.tft_model.TFTModel.fit" title="darts.models.forecasting.tft_model.TFTModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">fit()</span></code></a> and <a class="reference internal" href="#darts.models.forecasting.tft_model.TFTModel.predict" title="darts.models.forecasting.tft_model.TFTModel.predict"><code class="xref py py-func docutils literal notranslate"><span class="pre">predict()</span></code></a>.</p>
 </p></li>
+<li><p><strong>show_warnings</strong> – whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of
+your forecasting use case. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
 </ul>
 </dd>
 </dl>
-<dl class="simple">
-<dt>show_warnings</dt><dd><p>whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of
-your forecasting use case. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
-</dd>
-</dl>
 <p class="rubric">References</p>
 <dl class="citation">
 <dt class="label" id="r8b900b0279c7-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
@@ -1095,13 +1090,16 @@ <h1>Temporal Fusion Transformer (TFT)<a class="headerlink" href="#temporal-fusio
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tft_model.TFTModel.supports_static_covariates" title="darts.models.forecasting.tft_model.TFTModel.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tft_model.TFTModel.uses_future_covariates" title="darts.models.forecasting.tft_model.TFTModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tft_model.TFTModel.supports_transferrable_series_prediction" title="darts.models.forecasting.tft_model.TFTModel.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tft_model.TFTModel.uses_future_covariates" title="darts.models.forecasting.tft_model.TFTModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tft_model.TFTModel.uses_past_covariates" title="darts.models.forecasting.tft_model.TFTModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tft_model.TFTModel.uses_past_covariates" title="darts.models.forecasting.tft_model.TFTModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tft_model.TFTModel.uses_static_covariates" title="darts.models.forecasting.tft_model.TFTModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tft_model.TFTModel.uses_static_covariates" title="darts.models.forecasting.tft_model.TFTModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -1175,7 +1173,7 @@ <h1>Temporal Fusion Transformer (TFT)<a class="headerlink" href="#temporal-fusio
 <tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tft_model.TFTModel.lr_find" title="darts.models.forecasting.tft_model.TFTModel.lr_find"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lr_find</span></code></a>(series[, past_covariates, ...])</p></td>
 <td><p>A wrapper around PyTorch Lightning's <cite>Tuner.lr_find()</cite>.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tft_model.TFTModel.predict" title="darts.models.forecasting.tft_model.TFTModel.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n, *args, **kwargs)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tft_model.TFTModel.predict" title="darts.models.forecasting.tft_model.TFTModel.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, series, past_covariates, ...])</p></td>
 <td><p>Predict the <code class="docutils literal notranslate"><span class="pre">n</span></code> time step following the end of the training series, or of the specified <code class="docutils literal notranslate"><span class="pre">series</span></code>.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tft_model.TFTModel.predict_from_dataset" title="darts.models.forecasting.tft_model.TFTModel.predict_from_dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_from_dataset</span></code></a>(n, input_series_dataset)</p></td>
@@ -2056,7 +2054,7 @@ <h1>Temporal Fusion Transformer (TFT)<a class="headerlink" href="#temporal-fusio
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.tft_model.TFTModel.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/tft_model.html#TFTModel.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.tft_model.TFTModel.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">series</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">past_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trainer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">roll_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_loader_workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mc_dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predict_likelihood_parameters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.tft_model.TFTModel.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Predict the <code class="docutils literal notranslate"><span class="pre">n</span></code> time step following the end of the training series, or of the specified <code class="docutils literal notranslate"><span class="pre">series</span></code>.</p>
 <p>Prediction is performed with a PyTorch Lightning Trainer. It uses a default Trainer object from presets and
 <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code> used at model creation. You can also use a custom Trainer with optional parameter
@@ -2086,35 +2084,36 @@ <h1>Temporal Fusion Transformer (TFT)<a class="headerlink" href="#temporal-fusio
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>n</strong> – The number of time steps after the end of the training time series for which to produce predictions</p></li>
-<li><p><strong>series</strong> – Optionally, a series or sequence of series, representing the history of the target series whose
+<li><p><strong>n</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – The number of time steps after the end of the training time series for which to produce predictions</p></li>
+<li><p><strong>series</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Union</span></code>[<a class="reference internal" href="darts.timeseries.html#darts.timeseries.TimeSeries" title="darts.timeseries.TimeSeries"><code class="xref py py-class docutils literal notranslate"><span class="pre">TimeSeries</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">Sequence</span></code>[<a class="reference internal" href="darts.timeseries.html#darts.timeseries.TimeSeries" title="darts.timeseries.TimeSeries"><code class="xref py py-class docutils literal notranslate"><span class="pre">TimeSeries</span></code></a>], <code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code>]) – Optionally, a series or sequence of series, representing the history of the target series whose
 future is to be predicted. If specified, the method returns the forecasts of these
 series. Otherwise, the method returns the forecast of the (single) training series.</p></li>
-<li><p><strong>past_covariates</strong> – Optionally, the past-observed covariates series needed as inputs for the model.
+<li><p><strong>past_covariates</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Union</span></code>[<a class="reference internal" href="darts.timeseries.html#darts.timeseries.TimeSeries" title="darts.timeseries.TimeSeries"><code class="xref py py-class docutils literal notranslate"><span class="pre">TimeSeries</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">Sequence</span></code>[<a class="reference internal" href="darts.timeseries.html#darts.timeseries.TimeSeries" title="darts.timeseries.TimeSeries"><code class="xref py py-class docutils literal notranslate"><span class="pre">TimeSeries</span></code></a>], <code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code>]) – Optionally, the past-observed covariates series needed as inputs for the model.
 They must match the covariates used for training in terms of dimension.</p></li>
-<li><p><strong>future_covariates</strong> – Optionally, the future-known covariates series needed as inputs for the model.
+<li><p><strong>future_covariates</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Union</span></code>[<a class="reference internal" href="darts.timeseries.html#darts.timeseries.TimeSeries" title="darts.timeseries.TimeSeries"><code class="xref py py-class docutils literal notranslate"><span class="pre">TimeSeries</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">Sequence</span></code>[<a class="reference internal" href="darts.timeseries.html#darts.timeseries.TimeSeries" title="darts.timeseries.TimeSeries"><code class="xref py py-class docutils literal notranslate"><span class="pre">TimeSeries</span></code></a>], <code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code>]) – Optionally, the future-known covariates series needed as inputs for the model.
 They must match the covariates used for training in terms of dimension.</p></li>
-<li><p><strong>trainer</strong> – Optionally, a custom PyTorch-Lightning Trainer object to perform prediction. Using a custom <code class="docutils literal notranslate"><span class="pre">trainer</span></code>
+<li><p><strong>trainer</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">Trainer</span></code>]) – Optionally, a custom PyTorch-Lightning Trainer object to perform prediction. Using a custom <code class="docutils literal notranslate"><span class="pre">trainer</span></code>
 will override Darts’ default trainer.</p></li>
-<li><p><strong>batch_size</strong> – Size of batches during prediction. Defaults to the models’ training <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> value.</p></li>
-<li><p><strong>verbose</strong> – Optionally, whether to print the progress. Ignored if there is a <cite>ProgressBar</cite> callback in
+<li><p><strong>batch_size</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>]) – Size of batches during prediction. Defaults to the models’ training <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> value.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>]) – Optionally, whether to print the progress. Ignored if there is a <cite>ProgressBar</cite> callback in
 <cite>pl_trainer_kwargs</cite>.</p></li>
-<li><p><strong>n_jobs</strong> – The number of jobs to run in parallel. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. Defaults to <code class="docutils literal notranslate"><span class="pre">1</span></code>.</p></li>
-<li><p><strong>roll_size</strong> – For self-consuming predictions, i.e. <code class="docutils literal notranslate"><span class="pre">n</span> <span class="pre">&gt;</span> <span class="pre">output_chunk_length</span></code>, determines how many
+<li><p><strong>n_jobs</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – The number of jobs to run in parallel. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. Defaults to <code class="docutils literal notranslate"><span class="pre">1</span></code>.</p></li>
+<li><p><strong>roll_size</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>]) – For self-consuming predictions, i.e. <code class="docutils literal notranslate"><span class="pre">n</span> <span class="pre">&gt;</span> <span class="pre">output_chunk_length</span></code>, determines how many
 outputs of the model are fed back into it at every iteration of feeding the predicted target
 (and optionally future covariates) back into the model. If this parameter is not provided,
 it will be set <code class="docutils literal notranslate"><span class="pre">output_chunk_length</span></code> by default.</p></li>
-<li><p><strong>num_samples</strong> – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
+<li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
-<li><p><strong>num_loader_workers</strong> – Optionally, an integer specifying the <code class="docutils literal notranslate"><span class="pre">num_workers</span></code> to use in PyTorch <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code> instances,
+<li><p><strong>num_loader_workers</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Optionally, an integer specifying the <code class="docutils literal notranslate"><span class="pre">num_workers</span></code> to use in PyTorch <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code> instances,
 for the inference/prediction dataset loaders (if any).
 A larger number of workers can sometimes increase performance, but can also incur extra overheads
 and increase memory usage, as more batches are loaded in parallel.</p></li>
-<li><p><strong>mc_dropout</strong> – Optionally, enable monte carlo dropout for predictions using neural network based models.
+<li><p><strong>mc_dropout</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, enable monte carlo dropout for predictions using neural network based models.
 This allows bayesian approximation by specifying an implicit prior over learned models.</p></li>
-<li><p><strong>predict_likelihood_parameters</strong> – If set to <cite>True</cite>, the model predict the parameters of its Likelihood parameters instead of the target. Only
+<li><p><strong>predict_likelihood_parameters</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – If set to <cite>True</cite>, the model predict the parameters of its Likelihood parameters instead of the target. Only
 supported for probabilistic models with a likelihood, <cite>num_samples = 1</cite> and <cite>n&lt;=output_chunk_length</cite>.
-Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
+Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -2311,6 +2310,17 @@ <h1>Temporal Fusion Transformer (TFT)<a class="headerlink" href="#temporal-fusio
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.tft_model.TFTModel.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.tft_model.TFTModel.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.tft_model.TFTModel.to_cpu">
 <span class="sig-name descname"><span class="pre">to_cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.tft_model.TFTModel.to_cpu" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.theta.html b/generated_api/darts.models.forecasting.theta.html
index 87f1bbeed9..ce464a9c69 100644
--- a/generated_api/darts.models.forecasting.theta.html
+++ b/generated_api/darts.models.forecasting.theta.html
@@ -923,13 +923,16 @@ <h1>Theta Method<a class="headerlink" href="#theta-method" title="Permalink to t
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.FourTheta.supports_static_covariates" title="darts.models.forecasting.theta.FourTheta.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.FourTheta.uses_future_covariates" title="darts.models.forecasting.theta.FourTheta.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.FourTheta.supports_transferrable_series_prediction" title="darts.models.forecasting.theta.FourTheta.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.FourTheta.uses_future_covariates" title="darts.models.forecasting.theta.FourTheta.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.FourTheta.uses_past_covariates" title="darts.models.forecasting.theta.FourTheta.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.FourTheta.uses_past_covariates" title="darts.models.forecasting.theta.FourTheta.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.FourTheta.uses_static_covariates" title="darts.models.forecasting.theta.FourTheta.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.FourTheta.uses_static_covariates" title="darts.models.forecasting.theta.FourTheta.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -976,7 +979,7 @@ <h1>Theta Method<a class="headerlink" href="#theta-method" title="Permalink to t
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.FourTheta.load" title="darts.models.forecasting.theta.FourTheta.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path)</p></td>
 <td><p>Loads the model from a given path or file handle.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.FourTheta.predict" title="darts.models.forecasting.theta.FourTheta.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.FourTheta.predict" title="darts.models.forecasting.theta.FourTheta.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose, show_warnings])</p></td>
 <td><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.FourTheta.residuals" title="darts.models.forecasting.theta.FourTheta.residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">residuals</span></code></a>(series[, past_covariates, ...])</p></td>
@@ -1519,7 +1522,7 @@ <h1>Theta Method<a class="headerlink" href="#theta-method" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.theta.FourTheta.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/theta.html#FourTheta.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.theta.FourTheta.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/theta.html#FourTheta.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.theta.FourTheta.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1527,6 +1530,8 @@ <h1>Theta Method<a class="headerlink" href="#theta-method" title="Permalink to t
 <li><p><strong>n</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Forecast horizon - the number of time steps after the end of the series for which to produce predictions.</p></li>
 <li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, set the prediction verbosity. Not effective for all models.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -1695,6 +1700,17 @@ <h1>Theta Method<a class="headerlink" href="#theta-method" title="Permalink to t
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.theta.FourTheta.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.theta.FourTheta.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.theta.FourTheta.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.theta.FourTheta.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
@@ -1812,13 +1828,16 @@ <h1>Theta Method<a class="headerlink" href="#theta-method" title="Permalink to t
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.Theta.supports_static_covariates" title="darts.models.forecasting.theta.Theta.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.Theta.uses_future_covariates" title="darts.models.forecasting.theta.Theta.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.Theta.supports_transferrable_series_prediction" title="darts.models.forecasting.theta.Theta.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.Theta.uses_future_covariates" title="darts.models.forecasting.theta.Theta.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.Theta.uses_past_covariates" title="darts.models.forecasting.theta.Theta.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.Theta.uses_past_covariates" title="darts.models.forecasting.theta.Theta.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.Theta.uses_static_covariates" title="darts.models.forecasting.theta.Theta.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.Theta.uses_static_covariates" title="darts.models.forecasting.theta.Theta.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -1865,7 +1884,7 @@ <h1>Theta Method<a class="headerlink" href="#theta-method" title="Permalink to t
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.Theta.load" title="darts.models.forecasting.theta.Theta.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(path)</p></td>
 <td><p>Loads the model from a given path or file handle.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.Theta.predict" title="darts.models.forecasting.theta.Theta.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.Theta.predict" title="darts.models.forecasting.theta.Theta.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, num_samples, verbose, show_warnings])</p></td>
 <td><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.theta.Theta.residuals" title="darts.models.forecasting.theta.Theta.residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">residuals</span></code></a>(series[, past_covariates, ...])</p></td>
@@ -2405,7 +2424,7 @@ <h1>Theta Method<a class="headerlink" href="#theta-method" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.theta.Theta.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/theta.html#Theta.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.theta.Theta.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/theta.html#Theta.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.theta.Theta.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forecasts values for <cite>n</cite> time steps after the end of the training series.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -2413,6 +2432,8 @@ <h1>Theta Method<a class="headerlink" href="#theta-method" title="Permalink to t
 <li><p><strong>n</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Forecast horizon - the number of time steps after the end of the series for which to produce predictions.</p></li>
 <li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, set the prediction verbosity. Not effective for all models.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -2554,6 +2575,17 @@ <h1>Theta Method<a class="headerlink" href="#theta-method" title="Permalink to t
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.theta.Theta.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.theta.Theta.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.theta.Theta.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.theta.Theta.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.tide_model.html b/generated_api/darts.models.forecasting.tide_model.html
index c1a3c98e1a..29b4304682 100644
--- a/generated_api/darts.models.forecasting.tide_model.html
+++ b/generated_api/darts.models.forecasting.tide_model.html
@@ -907,7 +907,7 @@ <h1>Time-series Dense Encoder (TiDE)<a class="headerlink" href="#time-series-den
 <code class="docutils literal notranslate"><span class="pre">TimeSeries</span></code> is passed to the <a class="reference internal" href="#darts.models.forecasting.tide_model.TiDEModel.fit" title="darts.models.forecasting.tide_model.TiDEModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">fit()</span></code></a> method). Default: <code class="docutils literal notranslate"><span class="pre">1</span></code>.</p></li>
 <li><p><strong>force_reset</strong> – If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, any previously-existing model with the same name will be reset (all checkpoints will
 be discarded). Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
-<li><p><strong>save_checkpoints</strong> – Whether or not to automatically save the untrained model and checkpoints from training.
+<li><p><strong>save_checkpoints</strong> – Whether to automatically save the untrained model and checkpoints from training.
 To load the model from checkpoint, call <code class="xref py py-func docutils literal notranslate"><span class="pre">MyModelClass.load_from_checkpoint()</span></code>, where
 <code class="xref py py-class docutils literal notranslate"><span class="pre">MyModelClass</span></code> is the <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> class that was used (such as <code class="xref py py-class docutils literal notranslate"><span class="pre">TFTModel</span></code>,
 <code class="xref py py-class docutils literal notranslate"><span class="pre">NBEATSModel</span></code>, etc.). If set to <code class="docutils literal notranslate"><span class="pre">False</span></code>, the model can still be manually saved using
@@ -936,8 +936,8 @@ <h1>Time-series Dense Encoder (TiDE)<a class="headerlink" href="#time-series-den
 <li><p><strong>random_state</strong> – Control the randomness of the weights initialization. Check this
 <a class="reference external" href="https://scikit-learn.org/stable/glossary.html#term-random_state">link</a> for more details.
 Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li>
-<li><p><strong>pl_trainer_kwargs</strong> – <dl class="simple">
-<dt>By default <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> creates a PyTorch Lightning Trainer with several useful presets</dt><dd><p>that performs the training, validation and prediction processes. These presets include automatic
+<li><p><strong>pl_trainer_kwargs</strong> – <p>By default <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> creates a PyTorch Lightning Trainer with several useful presets
+that performs the training, validation and prediction processes. These presets include automatic
 checkpointing, tensorboard logging, setting the torch device and more.
 With <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code> you can add additional kwargs to instantiate the PyTorch Lightning trainer
 object. Check the <a class="reference external" href="https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html">PL Trainer documentation</a> for more information about the
@@ -945,8 +945,6 @@ <h1>Time-series Dense Encoder (TiDE)<a class="headerlink" href="#time-series-den
 Running on GPU(s) is also possible using <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code> by specifying keys <code class="docutils literal notranslate"><span class="pre">&quot;accelerator&quot;,</span>
 <span class="pre">&quot;devices&quot;,</span> <span class="pre">and</span> <span class="pre">&quot;auto_select_gpus&quot;</span></code>. Some examples for setting the devices inside the <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code>
 dict:</p>
-</dd>
-</dl>
 <ul>
 <li><p><code class="docutils literal notranslate"><span class="pre">{&quot;accelerator&quot;:</span> <span class="pre">&quot;cpu&quot;}</span></code> for CPU,</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">{&quot;accelerator&quot;:</span> <span class="pre">&quot;gpu&quot;,</span> <span class="pre">&quot;devices&quot;:</span> <span class="pre">[i]}</span></code> to use only GPU <code class="docutils literal notranslate"><span class="pre">i</span></code> (<code class="docutils literal notranslate"><span class="pre">i</span></code> must be an integer),</p></li>
@@ -977,14 +975,11 @@ <h1>Time-series Dense Encoder (TiDE)<a class="headerlink" href="#time-series-den
 <p>Note that you can also use a custom PyTorch Lightning Trainer for training and prediction with optional
 parameter <code class="docutils literal notranslate"><span class="pre">trainer</span></code> in <a class="reference internal" href="#darts.models.forecasting.tide_model.TiDEModel.fit" title="darts.models.forecasting.tide_model.TiDEModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">fit()</span></code></a> and <a class="reference internal" href="#darts.models.forecasting.tide_model.TiDEModel.predict" title="darts.models.forecasting.tide_model.TiDEModel.predict"><code class="xref py py-func docutils literal notranslate"><span class="pre">predict()</span></code></a>.</p>
 </p></li>
+<li><p><strong>show_warnings</strong> – whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of
+your forecasting use case. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
 </ul>
 </dd>
 </dl>
-<dl class="simple">
-<dt>show_warnings</dt><dd><p>whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of
-your forecasting use case. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
-</dd>
-</dl>
 <p class="rubric">References</p>
 <dl class="citation">
 <dt class="label" id="r725ce8c89e12-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
@@ -1064,13 +1059,16 @@ <h1>Time-series Dense Encoder (TiDE)<a class="headerlink" href="#time-series-den
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tide_model.TiDEModel.supports_static_covariates" title="darts.models.forecasting.tide_model.TiDEModel.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tide_model.TiDEModel.uses_future_covariates" title="darts.models.forecasting.tide_model.TiDEModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tide_model.TiDEModel.supports_transferrable_series_prediction" title="darts.models.forecasting.tide_model.TiDEModel.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tide_model.TiDEModel.uses_future_covariates" title="darts.models.forecasting.tide_model.TiDEModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tide_model.TiDEModel.uses_past_covariates" title="darts.models.forecasting.tide_model.TiDEModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tide_model.TiDEModel.uses_past_covariates" title="darts.models.forecasting.tide_model.TiDEModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tide_model.TiDEModel.uses_static_covariates" title="darts.models.forecasting.tide_model.TiDEModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tide_model.TiDEModel.uses_static_covariates" title="darts.models.forecasting.tide_model.TiDEModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -1144,7 +1142,7 @@ <h1>Time-series Dense Encoder (TiDE)<a class="headerlink" href="#time-series-den
 <tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tide_model.TiDEModel.lr_find" title="darts.models.forecasting.tide_model.TiDEModel.lr_find"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lr_find</span></code></a>(series[, past_covariates, ...])</p></td>
 <td><p>A wrapper around PyTorch Lightning's <cite>Tuner.lr_find()</cite>.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tide_model.TiDEModel.predict" title="darts.models.forecasting.tide_model.TiDEModel.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n, *args, **kwargs)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.tide_model.TiDEModel.predict" title="darts.models.forecasting.tide_model.TiDEModel.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(n[, series, past_covariates, ...])</p></td>
 <td><p>Predict the <code class="docutils literal notranslate"><span class="pre">n</span></code> time step following the end of the training series, or of the specified <code class="docutils literal notranslate"><span class="pre">series</span></code>.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.tide_model.TiDEModel.predict_from_dataset" title="darts.models.forecasting.tide_model.TiDEModel.predict_from_dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_from_dataset</span></code></a>(n, input_series_dataset)</p></td>
@@ -2025,7 +2023,7 @@ <h1>Time-series Dense Encoder (TiDE)<a class="headerlink" href="#time-series-den
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.tide_model.TiDEModel.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/darts/models/forecasting/tide_model.html#TiDEModel.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#darts.models.forecasting.tide_model.TiDEModel.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">series</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">past_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trainer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">roll_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_loader_workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mc_dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predict_likelihood_parameters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.tide_model.TiDEModel.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Predict the <code class="docutils literal notranslate"><span class="pre">n</span></code> time step following the end of the training series, or of the specified <code class="docutils literal notranslate"><span class="pre">series</span></code>.</p>
 <p>Prediction is performed with a PyTorch Lightning Trainer. It uses a default Trainer object from presets and
 <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code> used at model creation. You can also use a custom Trainer with optional parameter
@@ -2055,35 +2053,36 @@ <h1>Time-series Dense Encoder (TiDE)<a class="headerlink" href="#time-series-den
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>n</strong> – The number of time steps after the end of the training time series for which to produce predictions</p></li>
-<li><p><strong>series</strong> – Optionally, a series or sequence of series, representing the history of the target series whose
+<li><p><strong>n</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – The number of time steps after the end of the training time series for which to produce predictions</p></li>
+<li><p><strong>series</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Union</span></code>[<a class="reference internal" href="darts.timeseries.html#darts.timeseries.TimeSeries" title="darts.timeseries.TimeSeries"><code class="xref py py-class docutils literal notranslate"><span class="pre">TimeSeries</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">Sequence</span></code>[<a class="reference internal" href="darts.timeseries.html#darts.timeseries.TimeSeries" title="darts.timeseries.TimeSeries"><code class="xref py py-class docutils literal notranslate"><span class="pre">TimeSeries</span></code></a>], <code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code>]) – Optionally, a series or sequence of series, representing the history of the target series whose
 future is to be predicted. If specified, the method returns the forecasts of these
 series. Otherwise, the method returns the forecast of the (single) training series.</p></li>
-<li><p><strong>past_covariates</strong> – Optionally, the past-observed covariates series needed as inputs for the model.
+<li><p><strong>past_covariates</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Union</span></code>[<a class="reference internal" href="darts.timeseries.html#darts.timeseries.TimeSeries" title="darts.timeseries.TimeSeries"><code class="xref py py-class docutils literal notranslate"><span class="pre">TimeSeries</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">Sequence</span></code>[<a class="reference internal" href="darts.timeseries.html#darts.timeseries.TimeSeries" title="darts.timeseries.TimeSeries"><code class="xref py py-class docutils literal notranslate"><span class="pre">TimeSeries</span></code></a>], <code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code>]) – Optionally, the past-observed covariates series needed as inputs for the model.
 They must match the covariates used for training in terms of dimension.</p></li>
-<li><p><strong>future_covariates</strong> – Optionally, the future-known covariates series needed as inputs for the model.
+<li><p><strong>future_covariates</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Union</span></code>[<a class="reference internal" href="darts.timeseries.html#darts.timeseries.TimeSeries" title="darts.timeseries.TimeSeries"><code class="xref py py-class docutils literal notranslate"><span class="pre">TimeSeries</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">Sequence</span></code>[<a class="reference internal" href="darts.timeseries.html#darts.timeseries.TimeSeries" title="darts.timeseries.TimeSeries"><code class="xref py py-class docutils literal notranslate"><span class="pre">TimeSeries</span></code></a>], <code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code>]) – Optionally, the future-known covariates series needed as inputs for the model.
 They must match the covariates used for training in terms of dimension.</p></li>
-<li><p><strong>trainer</strong> – Optionally, a custom PyTorch-Lightning Trainer object to perform prediction. Using a custom <code class="docutils literal notranslate"><span class="pre">trainer</span></code>
+<li><p><strong>trainer</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">Trainer</span></code>]) – Optionally, a custom PyTorch-Lightning Trainer object to perform prediction. Using a custom <code class="docutils literal notranslate"><span class="pre">trainer</span></code>
 will override Darts’ default trainer.</p></li>
-<li><p><strong>batch_size</strong> – Size of batches during prediction. Defaults to the models’ training <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> value.</p></li>
-<li><p><strong>verbose</strong> – Optionally, whether to print the progress. Ignored if there is a <cite>ProgressBar</cite> callback in
+<li><p><strong>batch_size</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>]) – Size of batches during prediction. Defaults to the models’ training <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> value.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>]) – Optionally, whether to print the progress. Ignored if there is a <cite>ProgressBar</cite> callback in
 <cite>pl_trainer_kwargs</cite>.</p></li>
-<li><p><strong>n_jobs</strong> – The number of jobs to run in parallel. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. Defaults to <code class="docutils literal notranslate"><span class="pre">1</span></code>.</p></li>
-<li><p><strong>roll_size</strong> – For self-consuming predictions, i.e. <code class="docutils literal notranslate"><span class="pre">n</span> <span class="pre">&gt;</span> <span class="pre">output_chunk_length</span></code>, determines how many
+<li><p><strong>n_jobs</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – The number of jobs to run in parallel. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. Defaults to <code class="docutils literal notranslate"><span class="pre">1</span></code>.</p></li>
+<li><p><strong>roll_size</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>]) – For self-consuming predictions, i.e. <code class="docutils literal notranslate"><span class="pre">n</span> <span class="pre">&gt;</span> <span class="pre">output_chunk_length</span></code>, determines how many
 outputs of the model are fed back into it at every iteration of feeding the predicted target
 (and optionally future covariates) back into the model. If this parameter is not provided,
 it will be set <code class="docutils literal notranslate"><span class="pre">output_chunk_length</span></code> by default.</p></li>
-<li><p><strong>num_samples</strong> – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
+<li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
-<li><p><strong>num_loader_workers</strong> – Optionally, an integer specifying the <code class="docutils literal notranslate"><span class="pre">num_workers</span></code> to use in PyTorch <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code> instances,
+<li><p><strong>num_loader_workers</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Optionally, an integer specifying the <code class="docutils literal notranslate"><span class="pre">num_workers</span></code> to use in PyTorch <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code> instances,
 for the inference/prediction dataset loaders (if any).
 A larger number of workers can sometimes increase performance, but can also incur extra overheads
 and increase memory usage, as more batches are loaded in parallel.</p></li>
-<li><p><strong>mc_dropout</strong> – Optionally, enable monte carlo dropout for predictions using neural network based models.
+<li><p><strong>mc_dropout</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, enable monte carlo dropout for predictions using neural network based models.
 This allows bayesian approximation by specifying an implicit prior over learned models.</p></li>
-<li><p><strong>predict_likelihood_parameters</strong> – If set to <cite>True</cite>, the model predict the parameters of its Likelihood parameters instead of the target. Only
+<li><p><strong>predict_likelihood_parameters</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – If set to <cite>True</cite>, the model predict the parameters of its Likelihood parameters instead of the target. Only
 supported for probabilistic models with a likelihood, <cite>num_samples = 1</cite> and <cite>n&lt;=output_chunk_length</cite>.
-Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
+Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -2280,6 +2279,17 @@ <h1>Time-series Dense Encoder (TiDE)<a class="headerlink" href="#time-series-den
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.tide_model.TiDEModel.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.tide_model.TiDEModel.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.tide_model.TiDEModel.to_cpu">
 <span class="sig-name descname"><span class="pre">to_cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.tide_model.TiDEModel.to_cpu" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.transformer_model.html b/generated_api/darts.models.forecasting.transformer_model.html
index d0431f3241..569af52b5d 100644
--- a/generated_api/darts.models.forecasting.transformer_model.html
+++ b/generated_api/darts.models.forecasting.transformer_model.html
@@ -907,7 +907,7 @@ <h1>Transformer Model<a class="headerlink" href="#transformer-model" title="Perm
 <code class="docutils literal notranslate"><span class="pre">TimeSeries</span></code> is passed to the <a class="reference internal" href="#darts.models.forecasting.transformer_model.TransformerModel.fit" title="darts.models.forecasting.transformer_model.TransformerModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">fit()</span></code></a> method). Default: <code class="docutils literal notranslate"><span class="pre">1</span></code>.</p></li>
 <li><p><strong>force_reset</strong> – If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, any previously-existing model with the same name will be reset (all checkpoints will
 be discarded). Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
-<li><p><strong>save_checkpoints</strong> – Whether or not to automatically save the untrained model and checkpoints from training.
+<li><p><strong>save_checkpoints</strong> – Whether to automatically save the untrained model and checkpoints from training.
 To load the model from checkpoint, call <code class="xref py py-func docutils literal notranslate"><span class="pre">MyModelClass.load_from_checkpoint()</span></code>, where
 <code class="xref py py-class docutils literal notranslate"><span class="pre">MyModelClass</span></code> is the <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> class that was used (such as <code class="xref py py-class docutils literal notranslate"><span class="pre">TFTModel</span></code>,
 <code class="xref py py-class docutils literal notranslate"><span class="pre">NBEATSModel</span></code>, etc.). If set to <code class="docutils literal notranslate"><span class="pre">False</span></code>, the model can still be manually saved using
@@ -936,8 +936,8 @@ <h1>Transformer Model<a class="headerlink" href="#transformer-model" title="Perm
 <li><p><strong>random_state</strong> – Control the randomness of the weights initialization. Check this
 <a class="reference external" href="https://scikit-learn.org/stable/glossary.html#term-random_state">link</a> for more details.
 Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li>
-<li><p><strong>pl_trainer_kwargs</strong> – <dl class="simple">
-<dt>By default <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> creates a PyTorch Lightning Trainer with several useful presets</dt><dd><p>that performs the training, validation and prediction processes. These presets include automatic
+<li><p><strong>pl_trainer_kwargs</strong> – <p>By default <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchForecastingModel</span></code> creates a PyTorch Lightning Trainer with several useful presets
+that performs the training, validation and prediction processes. These presets include automatic
 checkpointing, tensorboard logging, setting the torch device and more.
 With <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code> you can add additional kwargs to instantiate the PyTorch Lightning trainer
 object. Check the <a class="reference external" href="https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html">PL Trainer documentation</a> for more information about the
@@ -945,8 +945,6 @@ <h1>Transformer Model<a class="headerlink" href="#transformer-model" title="Perm
 Running on GPU(s) is also possible using <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code> by specifying keys <code class="docutils literal notranslate"><span class="pre">&quot;accelerator&quot;,</span>
 <span class="pre">&quot;devices&quot;,</span> <span class="pre">and</span> <span class="pre">&quot;auto_select_gpus&quot;</span></code>. Some examples for setting the devices inside the <code class="docutils literal notranslate"><span class="pre">pl_trainer_kwargs</span></code>
 dict:</p>
-</dd>
-</dl>
 <ul>
 <li><p><code class="docutils literal notranslate"><span class="pre">{&quot;accelerator&quot;:</span> <span class="pre">&quot;cpu&quot;}</span></code> for CPU,</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">{&quot;accelerator&quot;:</span> <span class="pre">&quot;gpu&quot;,</span> <span class="pre">&quot;devices&quot;:</span> <span class="pre">[i]}</span></code> to use only GPU <code class="docutils literal notranslate"><span class="pre">i</span></code> (<code class="docutils literal notranslate"><span class="pre">i</span></code> must be an integer),</p></li>
@@ -977,14 +975,11 @@ <h1>Transformer Model<a class="headerlink" href="#transformer-model" title="Perm
 <p>Note that you can also use a custom PyTorch Lightning Trainer for training and prediction with optional
 parameter <code class="docutils literal notranslate"><span class="pre">trainer</span></code> in <a class="reference internal" href="#darts.models.forecasting.transformer_model.TransformerModel.fit" title="darts.models.forecasting.transformer_model.TransformerModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">fit()</span></code></a> and <a class="reference internal" href="#darts.models.forecasting.transformer_model.TransformerModel.predict" title="darts.models.forecasting.transformer_model.TransformerModel.predict"><code class="xref py py-func docutils literal notranslate"><span class="pre">predict()</span></code></a>.</p>
 </p></li>
+<li><p><strong>show_warnings</strong> – whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of
+your forecasting use case. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
 </ul>
 </dd>
 </dl>
-<dl class="simple">
-<dt>show_warnings</dt><dd><p>whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of
-your forecasting use case. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
-</dd>
-</dl>
 <p class="rubric">References</p>
 <dl class="citation">
 <dt class="label" id="r55d9314a45ae-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
@@ -1071,13 +1066,16 @@ <h1>Transformer Model<a class="headerlink" href="#transformer-model" title="Perm
 <tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.transformer_model.TransformerModel.supports_static_covariates" title="darts.models.forecasting.transformer_model.TransformerModel.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.transformer_model.TransformerModel.uses_future_covariates" title="darts.models.forecasting.transformer_model.TransformerModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.transformer_model.TransformerModel.supports_transferrable_series_prediction" title="darts.models.forecasting.transformer_model.TransformerModel.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.transformer_model.TransformerModel.uses_future_covariates" title="darts.models.forecasting.transformer_model.TransformerModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.transformer_model.TransformerModel.uses_past_covariates" title="darts.models.forecasting.transformer_model.TransformerModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.transformer_model.TransformerModel.uses_past_covariates" title="darts.models.forecasting.transformer_model.TransformerModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.transformer_model.TransformerModel.uses_static_covariates" title="darts.models.forecasting.transformer_model.TransformerModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.transformer_model.TransformerModel.uses_static_covariates" title="darts.models.forecasting.transformer_model.TransformerModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -2090,7 +2088,8 @@ <h1>Transformer Model<a class="headerlink" href="#transformer-model" title="Perm
 This allows bayesian approximation by specifying an implicit prior over learned models.</p></li>
 <li><p><strong>predict_likelihood_parameters</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – If set to <cite>True</cite>, the model predict the parameters of its Likelihood parameters instead of the target. Only
 supported for probabilistic models with a likelihood, <cite>num_samples = 1</cite> and <cite>n&lt;=output_chunk_length</cite>.
-Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
+Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -2281,6 +2280,17 @@ <h1>Transformer Model<a class="headerlink" href="#transformer-model" title="Perm
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.transformer_model.TransformerModel.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.transformer_model.TransformerModel.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.transformer_model.TransformerModel.to_cpu">
 <span class="sig-name descname"><span class="pre">to_cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.transformer_model.TransformerModel.to_cpu" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.varima.html b/generated_api/darts.models.forecasting.varima.html
index dfcf93db4d..73ada0ef7c 100644
--- a/generated_api/darts.models.forecasting.varima.html
+++ b/generated_api/darts.models.forecasting.varima.html
@@ -942,13 +942,16 @@ <h1>VARIMA<a class="headerlink" href="#varima" title="Permalink to this headline
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.varima.VARIMA.supports_static_covariates" title="darts.models.forecasting.varima.VARIMA.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.varima.VARIMA.uses_future_covariates" title="darts.models.forecasting.varima.VARIMA.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.varima.VARIMA.supports_transferrable_series_prediction" title="darts.models.forecasting.varima.VARIMA.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.varima.VARIMA.uses_future_covariates" title="darts.models.forecasting.varima.VARIMA.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.varima.VARIMA.uses_past_covariates" title="darts.models.forecasting.varima.VARIMA.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.varima.VARIMA.uses_past_covariates" title="darts.models.forecasting.varima.VARIMA.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.varima.VARIMA.uses_static_covariates" title="darts.models.forecasting.varima.VARIMA.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.varima.VARIMA.uses_static_covariates" title="darts.models.forecasting.varima.VARIMA.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -1541,7 +1544,7 @@ <h1>VARIMA<a class="headerlink" href="#varima" title="Permalink to this headline
 
 <dl class="py method">
 <dt class="sig sig-object py" id="darts.models.forecasting.varima.VARIMA.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">series</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.varima.VARIMA.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">series</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">future_covariates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#darts.models.forecasting.varima.VARIMA.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>If the <cite>series</cite> parameter is not set, forecasts values for <cite>n</cite> time steps after the end of the training
 series. If some future covariates were specified during the training, they must also be specified here.</p>
 <p>If the <cite>series</cite> parameter is set, forecasts values for <cite>n</cite> time steps after the end of the new target
@@ -1560,6 +1563,8 @@ <h1>VARIMA<a class="headerlink" href="#varima" title="Permalink to this headline
 </p></li>
 <li><p><strong>num_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1
 for deterministic models.</p></li>
+<li><p><strong>verbose</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, set the prediction verbosity. Not effective for all models.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -1701,6 +1706,17 @@ <h1>VARIMA<a class="headerlink" href="#varima" title="Permalink to this headline
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.varima.VARIMA.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.varima.VARIMA.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.varima.VARIMA.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.varima.VARIMA.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.models.forecasting.xgboost.html b/generated_api/darts.models.forecasting.xgboost.html
index f722595532..57040a92f2 100644
--- a/generated_api/darts.models.forecasting.xgboost.html
+++ b/generated_api/darts.models.forecasting.xgboost.html
@@ -983,13 +983,16 @@ <h1>XGBoost Model<a class="headerlink" href="#xgboost-model" title="Permalink to
 <tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.xgboost.XGBModel.supports_static_covariates" title="darts.models.forecasting.xgboost.XGBModel.supports_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_static_covariates</span></code></a></p></td>
 <td><p>Whether model supports static covariates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.xgboost.XGBModel.uses_future_covariates" title="darts.models.forecasting.xgboost.XGBModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.xgboost.XGBModel.supports_transferrable_series_prediction" title="darts.models.forecasting.xgboost.XGBModel.supports_transferrable_series_prediction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supports_transferrable_series_prediction</span></code></a></p></td>
+<td><p>Whether the model supports prediction for any input <cite>series</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.xgboost.XGBModel.uses_future_covariates" title="darts.models.forecasting.xgboost.XGBModel.uses_future_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_future_covariates</span></code></a></p></td>
 <td><p>Whether the model uses future covariates, once fitted.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.xgboost.XGBModel.uses_past_covariates" title="darts.models.forecasting.xgboost.XGBModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.xgboost.XGBModel.uses_past_covariates" title="darts.models.forecasting.xgboost.XGBModel.uses_past_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_past_covariates</span></code></a></p></td>
 <td><p>Whether the model uses past covariates, once fitted.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#darts.models.forecasting.xgboost.XGBModel.uses_static_covariates" title="darts.models.forecasting.xgboost.XGBModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#darts.models.forecasting.xgboost.XGBModel.uses_static_covariates" title="darts.models.forecasting.xgboost.XGBModel.uses_static_covariates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uses_static_covariates</span></code></a></p></td>
 <td><p>Whether the model uses static covariates, once fitted.</p></td>
 </tr>
 </tbody>
@@ -1662,6 +1665,7 @@ <h1>XGBoost Model<a class="headerlink" href="#xgboost-model" title="Permalink to
 Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
 <li><p><strong>**kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Additional keyword arguments passed to the <cite>predict</cite> method of the model. Only works with
 univariate target series.</p></li>
+<li><p><strong>show_warnings</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Optionally, control whether warnings are shown. Not effective for all models.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Return type</dt>
@@ -1801,6 +1805,17 @@ <h1>XGBoost Model<a class="headerlink" href="#xgboost-model" title="Permalink to
 </dl>
 </dd></dl>
 
+<dl class="py property">
+<dt class="sig sig-object py" id="darts.models.forecasting.xgboost.XGBModel.supports_transferrable_series_prediction">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">supports_transferrable_series_prediction</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.xgboost.XGBModel.supports_transferrable_series_prediction" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether the model supports prediction for any input <cite>series</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="darts.models.forecasting.xgboost.XGBModel.uses_future_covariates">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">uses_future_covariates</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#darts.models.forecasting.xgboost.XGBModel.uses_future_covariates" title="Permalink to this definition">¶</a></dt>
diff --git a/generated_api/darts.utils.data.shifted_dataset.html b/generated_api/darts.utils.data.shifted_dataset.html
index 48c948f6cf..1d19eefb4d 100644
--- a/generated_api/darts.utils.data.shifted_dataset.html
+++ b/generated_api/darts.utils.data.shifted_dataset.html
@@ -934,7 +934,7 @@ <h1>Shifted Training Dataset<a class="headerlink" href="#shifted-training-datase
 <li><p><strong>input_chunk_length</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – The length of the emitted past series.</p></li>
 <li><p><strong>output_chunk_length</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – The length of the emitted future series.</p></li>
 <li><p><strong>shift</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>) – The number of time steps by which to shift the output chunks relative to the input chunks.</p></li>
-<li><p><strong>shift_covariates</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Whether or not to shift the covariates forward the same way as the target.
+<li><p><strong>shift_covariates</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code>) – Whether to shift the covariates forward the same way as the target.
 FutureCovariatesModel’s require this set to True, while PastCovariatesModel’s require this set to False.</p></li>
 <li><p><strong>max_samples_per_ts</strong> (<code class="xref py py-data docutils literal notranslate"><span class="pre">Optional</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code>]) – This is an upper bound on the number of (input, output, input_covariates) tuples that can be produced
 per time series. It can be used in order to have an upper bound on the total size of the dataset and
diff --git a/genindex.html b/genindex.html
index 9ab44306e8..fe81babb6a 100644
--- a/genindex.html
+++ b/genindex.html
@@ -1435,9 +1435,13 @@ <h2 id="C">C</h2>
         <li><a href="generated_api/darts.models.forecasting.pl_forecasting_module.html#darts.models.forecasting.pl_forecasting_module.PLSplitCovariatesModule.current_epoch">(darts.models.forecasting.pl_forecasting_module.PLSplitCovariatesModule property)</a>
 </li>
       </ul></li>
+      <li><a href="generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.CustomBlockRNNModule">CustomBlockRNNModule (class in darts.models.forecasting.block_rnn_model)</a>
+</li>
       <li><a href="generated_api/darts.models.components.transformer.html#darts.models.components.transformer.CustomFeedForwardDecoderLayer">CustomFeedForwardDecoderLayer (class in darts.models.components.transformer)</a>
 </li>
       <li><a href="generated_api/darts.models.components.transformer.html#darts.models.components.transformer.CustomFeedForwardEncoderLayer">CustomFeedForwardEncoderLayer (class in darts.models.components.transformer)</a>
+</li>
+      <li><a href="generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.CustomRNNModule">CustomRNNModule (class in darts.models.forecasting.rnn_model)</a>
 </li>
       <li><a href="generated_api/darts.dataprocessing.encoders.encoders.html#darts.dataprocessing.encoders.encoders.CyclicTemporalEncoder">CyclicTemporalEncoder (class in darts.dataprocessing.encoders.encoders)</a>
 </li>
@@ -1984,13 +1988,6 @@ <h2 id="D">D</h2>
 
       <ul>
         <li><a href="generated_api/darts.models.forecasting.baselines.html#module-darts.models.forecasting.baselines">module</a>
-</li>
-      </ul></li>
-      <li>
-    darts.models.forecasting.block_rnn_model
-
-      <ul>
-        <li><a href="generated_api/darts.models.forecasting.block_rnn_model.html#module-darts.models.forecasting.block_rnn_model">module</a>
 </li>
       </ul></li>
       <li>
@@ -2119,13 +2116,6 @@ <h2 id="D">D</h2>
 
       <ul>
         <li><a href="generated_api/darts.models.forecasting.regression_model.html#module-darts.models.forecasting.regression_model">module</a>
-</li>
-      </ul></li>
-      <li>
-    darts.models.forecasting.rnn_model
-
-      <ul>
-        <li><a href="generated_api/darts.models.forecasting.rnn_model.html#module-darts.models.forecasting.rnn_model">module</a>
 </li>
       </ul></li>
       <li>
@@ -2777,11 +2767,9 @@ <h2 id="E">E</h2>
 </li>
       <li><a href="generated_api/darts.ad.aggregators.ensemble_sklearn_aggregator.html#darts.ad.aggregators.ensemble_sklearn_aggregator.EnsembleSklearnAggregator">EnsembleSklearnAggregator (class in darts.ad.aggregators.ensemble_sklearn_aggregator)</a>
 </li>
-      <li><a href="generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel.epochs_trained">epochs_trained (darts.models.forecasting.block_rnn_model.BlockRNNModel property)</a>
+      <li><a href="generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel.epochs_trained">epochs_trained (darts.models.forecasting.dlinear.DLinearModel property)</a>
 
       <ul>
-        <li><a href="generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel.epochs_trained">(darts.models.forecasting.dlinear.DLinearModel property)</a>
-</li>
         <li><a href="generated_api/darts.models.forecasting.nbeats.html#darts.models.forecasting.nbeats.NBEATSModel.epochs_trained">(darts.models.forecasting.nbeats.NBEATSModel property)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.nhits.html#darts.models.forecasting.nhits.NHiTSModel.epochs_trained">(darts.models.forecasting.nhits.NHiTSModel property)</a>
@@ -2799,8 +2787,6 @@ <h2 id="E">E</h2>
         <li><a href="generated_api/darts.models.forecasting.pl_forecasting_module.html#darts.models.forecasting.pl_forecasting_module.PLPastCovariatesModule.epochs_trained">(darts.models.forecasting.pl_forecasting_module.PLPastCovariatesModule property)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.pl_forecasting_module.html#darts.models.forecasting.pl_forecasting_module.PLSplitCovariatesModule.epochs_trained">(darts.models.forecasting.pl_forecasting_module.PLSplitCovariatesModule property)</a>
-</li>
-        <li><a href="generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel.epochs_trained">(darts.models.forecasting.rnn_model.RNNModel property)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.tcn_model.html#darts.models.forecasting.tcn_model.TCNModel.epochs_trained">(darts.models.forecasting.tcn_model.TCNModel property)</a>
 </li>
@@ -3167,6 +3153,12 @@ <h2 id="F">F</h2>
 </li>
       <li><a href="generated_api/darts.utils.data.inference_dataset.html#darts.utils.data.inference_dataset.GenericInferenceDataset.find_list_index">find_list_index() (darts.utils.data.inference_dataset.GenericInferenceDataset static method)</a>
 </li>
+      <li><a href="generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel.first_prediction_index">first_prediction_index (darts.models.forecasting.block_rnn_model.BlockRNNModel property)</a>
+
+      <ul>
+        <li><a href="generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel.first_prediction_index">(darts.models.forecasting.rnn_model.RNNModel property)</a>
+</li>
+      </ul></li>
       <li><a href="generated_api/darts.timeseries.html#darts.timeseries.TimeSeries.first_value">first_value() (darts.timeseries.TimeSeries method)</a>
 </li>
       <li><a href="generated_api/darts.timeseries.html#darts.timeseries.TimeSeries.first_values">first_values() (darts.timeseries.TimeSeries method)</a>
@@ -3491,6 +3483,8 @@ <h2 id="F">F</h2>
         <li><a href="generated_api/darts.models.components.transformer.html#darts.models.components.transformer.CustomFeedForwardDecoderLayer.forward">(darts.models.components.transformer.CustomFeedForwardDecoderLayer method)</a>
 </li>
         <li><a href="generated_api/darts.models.components.transformer.html#darts.models.components.transformer.CustomFeedForwardEncoderLayer.forward">(darts.models.components.transformer.CustomFeedForwardEncoderLayer method)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.CustomBlockRNNModule.forward">(darts.models.forecasting.block_rnn_model.CustomBlockRNNModule method)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.pl_forecasting_module.html#darts.models.forecasting.pl_forecasting_module.PLDualCovariatesModule.forward">(darts.models.forecasting.pl_forecasting_module.PLDualCovariatesModule method)</a>
 </li>
@@ -3503,6 +3497,8 @@ <h2 id="F">F</h2>
         <li><a href="generated_api/darts.models.forecasting.pl_forecasting_module.html#darts.models.forecasting.pl_forecasting_module.PLPastCovariatesModule.forward">(darts.models.forecasting.pl_forecasting_module.PLPastCovariatesModule method)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.pl_forecasting_module.html#darts.models.forecasting.pl_forecasting_module.PLSplitCovariatesModule.forward">(darts.models.forecasting.pl_forecasting_module.PLSplitCovariatesModule method)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.CustomRNNModule.forward">(darts.models.forecasting.rnn_model.CustomRNNModule method)</a>
 </li>
         <li><a href="generated_api/darts.utils.losses.html#darts.utils.losses.MAELoss.forward">(darts.utils.losses.MAELoss method)</a>
 </li>
@@ -4527,20 +4523,22 @@ <h2 id="I">I</h2>
 </li>
       <li><a href="generated_api/darts.utils.callbacks.html#darts.utils.callbacks.TFMProgressBar.init_validation_tqdm">init_validation_tqdm() (darts.utils.callbacks.TFMProgressBar method)</a>
 </li>
+      <li><a href="generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel.initialize_encoders">initialize_encoders() (darts.models.forecasting.block_rnn_model.BlockRNNModel method)</a>
+
+      <ul>
+        <li><a href="generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel.initialize_encoders">(darts.models.forecasting.rnn_model.RNNModel method)</a>
+</li>
+      </ul></li>
       <li><a href="generated_api/darts.utils.torch.html#darts.utils.torch.MonteCarloDropout.inplace">inplace (darts.utils.torch.MonteCarloDropout attribute)</a>
 </li>
-      <li><a href="generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel.input_chunk_length">input_chunk_length (darts.models.forecasting.block_rnn_model.BlockRNNModel property)</a>
+      <li><a href="generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel.input_chunk_length">input_chunk_length (darts.models.forecasting.dlinear.DLinearModel property)</a>
 
       <ul>
-        <li><a href="generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel.input_chunk_length">(darts.models.forecasting.dlinear.DLinearModel property)</a>
-</li>
         <li><a href="generated_api/darts.models.forecasting.nbeats.html#darts.models.forecasting.nbeats.NBEATSModel.input_chunk_length">(darts.models.forecasting.nbeats.NBEATSModel property)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.nhits.html#darts.models.forecasting.nhits.NHiTSModel.input_chunk_length">(darts.models.forecasting.nhits.NHiTSModel property)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.nlinear.html#darts.models.forecasting.nlinear.NLinearModel.input_chunk_length">(darts.models.forecasting.nlinear.NLinearModel property)</a>
-</li>
-        <li><a href="generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel.input_chunk_length">(darts.models.forecasting.rnn_model.RNNModel property)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.tcn_model.html#darts.models.forecasting.tcn_model.TCNModel.input_chunk_length">(darts.models.forecasting.tcn_model.TCNModel property)</a>
 </li>
@@ -4749,18 +4747,14 @@ <h2 id="L">L</h2>
 </li>
       <li><a href="generated_api/darts.utils.likelihood_models.html#darts.utils.likelihood_models.Likelihood">Likelihood (class in darts.utils.likelihood_models)</a>
 </li>
-      <li><a href="generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel.likelihood">likelihood (darts.models.forecasting.block_rnn_model.BlockRNNModel property)</a>
+      <li><a href="generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel.likelihood">likelihood (darts.models.forecasting.dlinear.DLinearModel property)</a>
 
       <ul>
-        <li><a href="generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel.likelihood">(darts.models.forecasting.dlinear.DLinearModel property)</a>
-</li>
         <li><a href="generated_api/darts.models.forecasting.nbeats.html#darts.models.forecasting.nbeats.NBEATSModel.likelihood">(darts.models.forecasting.nbeats.NBEATSModel property)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.nhits.html#darts.models.forecasting.nhits.NHiTSModel.likelihood">(darts.models.forecasting.nhits.NHiTSModel property)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.nlinear.html#darts.models.forecasting.nlinear.NLinearModel.likelihood">(darts.models.forecasting.nlinear.NLinearModel property)</a>
-</li>
-        <li><a href="generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel.likelihood">(darts.models.forecasting.rnn_model.RNNModel property)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.tcn_model.html#darts.models.forecasting.tcn_model.TCNModel.likelihood">(darts.models.forecasting.tcn_model.TCNModel property)</a>
 </li>
@@ -5373,6 +5367,12 @@ <h2 id="M">M</h2>
         <li><a href="generated_api/darts.models.forecasting.varima.html#darts.models.forecasting.varima.VARIMA.min_train_samples">(darts.models.forecasting.varima.VARIMA property)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.xgboost.html#darts.models.forecasting.xgboost.XGBModel.min_train_samples">(darts.models.forecasting.xgboost.XGBModel property)</a>
+</li>
+      </ul></li>
+      <li><a href="generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel.min_train_series_length">min_train_series_length (darts.models.forecasting.block_rnn_model.BlockRNNModel property)</a>
+
+      <ul>
+        <li><a href="generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel.min_train_series_length">(darts.models.forecasting.rnn_model.RNNModel property)</a>
 </li>
       </ul></li>
       <li><a href="generated_api/darts.dataprocessing.transformers.reconciliation.html#darts.dataprocessing.transformers.reconciliation.MinTReconciliator">MinTReconciliator (class in darts.dataprocessing.transformers.reconciliation)</a>
@@ -5395,18 +5395,14 @@ <h2 id="M">M</h2>
         <li><a href="generated_api/darts.explainability.tft_explainer.html#darts.explainability.tft_explainer.TFTExplainer.model">(darts.explainability.tft_explainer.TFTExplainer attribute)</a>
 </li>
       </ul></li>
-      <li><a href="generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel.model_created">model_created (darts.models.forecasting.block_rnn_model.BlockRNNModel property)</a>
+      <li><a href="generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel.model_created">model_created (darts.models.forecasting.dlinear.DLinearModel property)</a>
 
       <ul>
-        <li><a href="generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel.model_created">(darts.models.forecasting.dlinear.DLinearModel property)</a>
-</li>
         <li><a href="generated_api/darts.models.forecasting.nbeats.html#darts.models.forecasting.nbeats.NBEATSModel.model_created">(darts.models.forecasting.nbeats.NBEATSModel property)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.nhits.html#darts.models.forecasting.nhits.NHiTSModel.model_created">(darts.models.forecasting.nhits.NHiTSModel property)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.nlinear.html#darts.models.forecasting.nlinear.NLinearModel.model_created">(darts.models.forecasting.nlinear.NLinearModel property)</a>
-</li>
-        <li><a href="generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel.model_created">(darts.models.forecasting.rnn_model.RNNModel property)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.tcn_model.html#darts.models.forecasting.tcn_model.TCNModel.model_created">(darts.models.forecasting.tcn_model.TCNModel property)</a>
 </li>
@@ -5431,8 +5427,6 @@ <h2 id="M">M</h2>
         <li><a href="generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMovingAverage.model_params">(darts.models.forecasting.baselines.NaiveMovingAverage property)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveSeasonal.model_params">(darts.models.forecasting.baselines.NaiveSeasonal property)</a>
-</li>
-        <li><a href="generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel.model_params">(darts.models.forecasting.block_rnn_model.BlockRNNModel property)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.catboost_model.html#darts.models.forecasting.catboost_model.CatBoostModel.model_params">(darts.models.forecasting.catboost_model.CatBoostModel property)</a>
 </li>
@@ -5467,8 +5461,6 @@ <h2 id="M">M</h2>
         <li><a href="generated_api/darts.models.forecasting.regression_model.html#darts.models.forecasting.regression_model.RegressionModel.model_params">(darts.models.forecasting.regression_model.RegressionModel property)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.regression_model.html#darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.model_params">(darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates property)</a>
-</li>
-        <li><a href="generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel.model_params">(darts.models.forecasting.rnn_model.RNNModel property)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.sf_auto_arima.html#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.model_params">(darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA property)</a>
 </li>
@@ -5660,8 +5652,6 @@ <h2 id="M">M</h2>
         <li><a href="generated_api/darts.models.forecasting.auto_arima.html#module-darts.models.forecasting.auto_arima">darts.models.forecasting.auto_arima</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.baselines.html#module-darts.models.forecasting.baselines">darts.models.forecasting.baselines</a>
-</li>
-        <li><a href="generated_api/darts.models.forecasting.block_rnn_model.html#module-darts.models.forecasting.block_rnn_model">darts.models.forecasting.block_rnn_model</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.catboost_model.html#module-darts.models.forecasting.catboost_model">darts.models.forecasting.catboost_model</a>
 </li>
@@ -5698,8 +5688,6 @@ <h2 id="M">M</h2>
         <li><a href="generated_api/darts.models.forecasting.regression_ensemble_model.html#module-darts.models.forecasting.regression_ensemble_model">darts.models.forecasting.regression_ensemble_model</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.regression_model.html#module-darts.models.forecasting.regression_model">darts.models.forecasting.regression_model</a>
-</li>
-        <li><a href="generated_api/darts.models.forecasting.rnn_model.html#module-darts.models.forecasting.rnn_model">darts.models.forecasting.rnn_model</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.sf_auto_arima.html#module-darts.models.forecasting.sf_auto_arima">darts.models.forecasting.sf_auto_arima</a>
 </li>
@@ -8964,8 +8952,6 @@ <h2 id="S">S</h2>
         <li><a href="generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMovingAverage.supports_future_covariates">(darts.models.forecasting.baselines.NaiveMovingAverage property)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveSeasonal.supports_future_covariates">(darts.models.forecasting.baselines.NaiveSeasonal property)</a>
-</li>
-        <li><a href="generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel.supports_future_covariates">(darts.models.forecasting.block_rnn_model.BlockRNNModel attribute)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.catboost_model.html#darts.models.forecasting.catboost_model.CatBoostModel.supports_future_covariates">(darts.models.forecasting.catboost_model.CatBoostModel property)</a>
 </li>
@@ -9328,8 +9314,6 @@ <h2 id="S">S</h2>
         <li><a href="generated_api/darts.models.forecasting.regression_model.html#darts.models.forecasting.regression_model.RegressionModel.supports_past_covariates">(darts.models.forecasting.regression_model.RegressionModel property)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.regression_model.html#darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.supports_past_covariates">(darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates property)</a>
-</li>
-        <li><a href="generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel.supports_past_covariates">(darts.models.forecasting.rnn_model.RNNModel attribute)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.sf_auto_arima.html#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.supports_past_covariates">(darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA property)</a>
 </li>
@@ -9440,6 +9424,88 @@ <h2 id="S">S</h2>
         <li><a href="generated_api/darts.models.forecasting.varima.html#darts.models.forecasting.varima.VARIMA.supports_static_covariates">(darts.models.forecasting.varima.VARIMA property)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.xgboost.html#darts.models.forecasting.xgboost.XGBModel.supports_static_covariates">(darts.models.forecasting.xgboost.XGBModel property)</a>
+</li>
+      </ul></li>
+      <li><a href="generated_api/darts.models.forecasting.arima.html#darts.models.forecasting.arima.ARIMA.supports_transferrable_series_prediction">supports_transferrable_series_prediction (darts.models.forecasting.arima.ARIMA property)</a>
+
+      <ul>
+        <li><a href="generated_api/darts.models.forecasting.auto_arima.html#darts.models.forecasting.auto_arima.AutoARIMA.supports_transferrable_series_prediction">(darts.models.forecasting.auto_arima.AutoARIMA property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveDrift.supports_transferrable_series_prediction">(darts.models.forecasting.baselines.NaiveDrift property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveEnsembleModel.supports_transferrable_series_prediction">(darts.models.forecasting.baselines.NaiveEnsembleModel property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMean.supports_transferrable_series_prediction">(darts.models.forecasting.baselines.NaiveMean property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMovingAverage.supports_transferrable_series_prediction">(darts.models.forecasting.baselines.NaiveMovingAverage property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveSeasonal.supports_transferrable_series_prediction">(darts.models.forecasting.baselines.NaiveSeasonal property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel.supports_transferrable_series_prediction">(darts.models.forecasting.block_rnn_model.BlockRNNModel property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.catboost_model.html#darts.models.forecasting.catboost_model.CatBoostModel.supports_transferrable_series_prediction">(darts.models.forecasting.catboost_model.CatBoostModel property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.croston.html#darts.models.forecasting.croston.Croston.supports_transferrable_series_prediction">(darts.models.forecasting.croston.Croston property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel.supports_transferrable_series_prediction">(darts.models.forecasting.dlinear.DLinearModel property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.ensemble_model.html#darts.models.forecasting.ensemble_model.EnsembleModel.supports_transferrable_series_prediction">(darts.models.forecasting.ensemble_model.EnsembleModel property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.exponential_smoothing.html#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing.supports_transferrable_series_prediction">(darts.models.forecasting.exponential_smoothing.ExponentialSmoothing property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.fft.html#darts.models.forecasting.fft.FFT.supports_transferrable_series_prediction">(darts.models.forecasting.fft.FFT property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.kalman_forecaster.html#darts.models.forecasting.kalman_forecaster.KalmanForecaster.supports_transferrable_series_prediction">(darts.models.forecasting.kalman_forecaster.KalmanForecaster property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.lgbm.html#darts.models.forecasting.lgbm.LightGBMModel.supports_transferrable_series_prediction">(darts.models.forecasting.lgbm.LightGBMModel property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.linear_regression_model.html#darts.models.forecasting.linear_regression_model.LinearRegressionModel.supports_transferrable_series_prediction">(darts.models.forecasting.linear_regression_model.LinearRegressionModel property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.nbeats.html#darts.models.forecasting.nbeats.NBEATSModel.supports_transferrable_series_prediction">(darts.models.forecasting.nbeats.NBEATSModel property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.nhits.html#darts.models.forecasting.nhits.NHiTSModel.supports_transferrable_series_prediction">(darts.models.forecasting.nhits.NHiTSModel property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.nlinear.html#darts.models.forecasting.nlinear.NLinearModel.supports_transferrable_series_prediction">(darts.models.forecasting.nlinear.NLinearModel property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.prophet_model.html#darts.models.forecasting.prophet_model.Prophet.supports_transferrable_series_prediction">(darts.models.forecasting.prophet_model.Prophet property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.random_forest.html#darts.models.forecasting.random_forest.RandomForest.supports_transferrable_series_prediction">(darts.models.forecasting.random_forest.RandomForest property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.regression_ensemble_model.html#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel.supports_transferrable_series_prediction">(darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.regression_model.html#darts.models.forecasting.regression_model.RegressionModel.supports_transferrable_series_prediction">(darts.models.forecasting.regression_model.RegressionModel property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.regression_model.html#darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates.supports_transferrable_series_prediction">(darts.models.forecasting.regression_model.RegressionModelWithCategoricalCovariates property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel.supports_transferrable_series_prediction">(darts.models.forecasting.rnn_model.RNNModel property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.sf_auto_arima.html#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA.supports_transferrable_series_prediction">(darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.sf_auto_ces.html#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES.supports_transferrable_series_prediction">(darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.sf_auto_ets.html#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS.supports_transferrable_series_prediction">(darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.sf_auto_theta.html#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta.supports_transferrable_series_prediction">(darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.tbats_model.html#darts.models.forecasting.tbats_model.BATS.supports_transferrable_series_prediction">(darts.models.forecasting.tbats_model.BATS property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.tbats_model.html#darts.models.forecasting.tbats_model.TBATS.supports_transferrable_series_prediction">(darts.models.forecasting.tbats_model.TBATS property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.tcn_model.html#darts.models.forecasting.tcn_model.TCNModel.supports_transferrable_series_prediction">(darts.models.forecasting.tcn_model.TCNModel property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.tft_model.html#darts.models.forecasting.tft_model.TFTModel.supports_transferrable_series_prediction">(darts.models.forecasting.tft_model.TFTModel property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.FourTheta.supports_transferrable_series_prediction">(darts.models.forecasting.theta.FourTheta property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.Theta.supports_transferrable_series_prediction">(darts.models.forecasting.theta.Theta property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.tide_model.html#darts.models.forecasting.tide_model.TiDEModel.supports_transferrable_series_prediction">(darts.models.forecasting.tide_model.TiDEModel property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.transformer_model.html#darts.models.forecasting.transformer_model.TransformerModel.supports_transferrable_series_prediction">(darts.models.forecasting.transformer_model.TransformerModel property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.varima.html#darts.models.forecasting.varima.VARIMA.supports_transferrable_series_prediction">(darts.models.forecasting.varima.VARIMA property)</a>
+</li>
+        <li><a href="generated_api/darts.models.forecasting.xgboost.html#darts.models.forecasting.xgboost.XGBModel.supports_transferrable_series_prediction">(darts.models.forecasting.xgboost.XGBModel property)</a>
 </li>
       </ul></li>
       <li><a href="generated_api/darts.dataprocessing.dtw.cost_matrix.html#darts.dataprocessing.dtw.cost_matrix.DenseCostMatrix.swapaxes">swapaxes() (darts.dataprocessing.dtw.cost_matrix.DenseCostMatrix method)</a>
@@ -10236,6 +10302,12 @@ <h2 id="U">U</h2>
         <li><a href="generated_api/darts.models.forecasting.pl_forecasting_module.html#darts.models.forecasting.pl_forecasting_module.PLPastCovariatesModule.untoggle_optimizer">(darts.models.forecasting.pl_forecasting_module.PLPastCovariatesModule method)</a>
 </li>
         <li><a href="generated_api/darts.models.forecasting.pl_forecasting_module.html#darts.models.forecasting.pl_forecasting_module.PLSplitCovariatesModule.untoggle_optimizer">(darts.models.forecasting.pl_forecasting_module.PLSplitCovariatesModule method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel.untrained_model">untrained_model() (darts.models.forecasting.block_rnn_model.BlockRNNModel method)</a>
+
+      <ul>
+        <li><a href="generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel.untrained_model">(darts.models.forecasting.rnn_model.RNNModel method)</a>
 </li>
       </ul></li>
       <li><a href="generated_api/darts.datasets.dataset_loaders.html#darts.datasets.dataset_loaders.DatasetLoaderMetadata.uri">uri (darts.datasets.dataset_loaders.DatasetLoaderMetadata attribute)</a>
diff --git a/index.html b/index.html
index a5a129f2f4..30454a112c 100644
--- a/index.html
+++ b/index.html
@@ -258,7 +258,7 @@ <h1>Time Series Made Easy in Python<a class="headerlink" href="#time-series-made
 <a class="reference external image-reference" href="https://badge.fury.io/py/darts"><img alt="PyPI version" src="https://badge.fury.io/py/u8darts.svg" /></a>
 <a class="reference external image-reference" href="https://anaconda.org/conda-forge/u8darts-all"><img alt="Conda Version" src="https://img.shields.io/conda/vn/conda-forge/u8darts-all.svg" /></a>
 <a class="reference external image-reference" href="https://img.shields.io/badge/python-3.8+-blue.svg"><img alt="Supported versions" src="https://img.shields.io/badge/python-3.8+-blue.svg" /></a>
-<a class="reference external image-reference" href="https://img.shields.io/docker/v/unit8/darts?label=docker&amp;sort=date"><img alt="Docker Image Version (latest by date)" src="https://img.shields.io/docker/v/unit8/darts?label=docker&amp;sort=date" /></a>
+<a class="reference external image-reference" href="https://hub.docker.com/r/unit8/darts"><img alt="Docker Image Version (latest by date)" src="https://img.shields.io/docker/v/unit8/darts?label=docker&amp;sort=date" /></a>
 <a class="reference external image-reference" href="https://img.shields.io/github/release-date/unit8co/darts"><img alt="GitHub Release Date" src="https://img.shields.io/github/release-date/unit8co/darts" /></a>
 <a class="reference external image-reference" href="https://img.shields.io/github/actions/workflow/status/unit8co/darts/release.yml?branch=master"><img alt="GitHub Workflow Status" src="https://img.shields.io/github/actions/workflow/status/unit8co/darts/release.yml?branch=master" /></a>
 <a class="reference external image-reference" href="https://pepy.tech/project/darts"><img alt="Downloads" src="https://pepy.tech/badge/darts" /></a>
@@ -463,28 +463,28 @@ <h2>Forecasting Models<a class="headerlink" href="#forecasting-models" title="Pe
 </tr>
 <tr class="row-odd"><td><p><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMean">NaiveMean</a></p></td>
 <td></td>
-<td><p>🟩 🟥</p></td>
+<td><p>🟩 🟩</p></td>
 <td><p>🟥 🟥 🟥</p></td>
 <td><p>🟥 🟥</p></td>
 <td><p>🟥</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveSeasonal">NaiveSeasonal</a></p></td>
 <td></td>
-<td><p>🟩 🟥</p></td>
+<td><p>🟩 🟩</p></td>
 <td><p>🟥 🟥 🟥</p></td>
 <td><p>🟥 🟥</p></td>
 <td><p>🟥</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveDrift">NaiveDrift</a></p></td>
 <td></td>
-<td><p>🟩 🟥</p></td>
+<td><p>🟩 🟩</p></td>
 <td><p>🟥 🟥 🟥</p></td>
 <td><p>🟥 🟥</p></td>
 <td><p>🟥</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMovingAverage">NaiveMovingAverage</a></p></td>
 <td></td>
-<td><p>🟩 🟥</p></td>
+<td><p>🟩 🟩</p></td>
 <td><p>🟥 🟥 🟥</p></td>
 <td><p>🟥 🟥</p></td>
 <td><p>🟥</p></td>
@@ -507,7 +507,7 @@ <h2>Forecasting Models<a class="headerlink" href="#forecasting-models" title="Pe
 <td></td>
 <td><p>🟥 🟩</p></td>
 <td><p>🟥 🟩 🟥</p></td>
-<td><p>🟥 🟥</p></td>
+<td><p>🟩 🟥</p></td>
 <td><p>🟥</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.auto_arima.html#darts.models.forecasting.auto_arima.AutoARIMA">AutoARIMA</a></p></td>
@@ -566,7 +566,7 @@ <h2>Forecasting Models<a class="headerlink" href="#forecasting-models" title="Pe
 <td><p>🟩 🟥</p></td>
 <td><p>🟥</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.prophet_model.html#darts.models.forecasting.prophet_model.Prophet">Prophet</a> (see <a class="reference external" href="https://github.com/unit8co/darts/blob/master/INSTALL.md#enabling-support-for-facebook-prophet">install notes</a>)</p></td>
+<tr class="row-even"><td><p><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.prophet_model.html#darts.models.forecasting.prophet_model.Prophet">Prophet</a></p></td>
 <td><p><a class="reference external" href="https://github.com/facebook/prophet">Prophet repo</a></p></td>
 <td><p>🟩 🟥</p></td>
 <td><p>🟥 🟩 🟥</p></td>
@@ -622,7 +622,7 @@ <h2>Forecasting Models<a class="headerlink" href="#forecasting-models" title="Pe
 <td><p>🟥 🟥</p></td>
 <td><p>🟩</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.lgbm.html#darts.models.forecasting.lgbm.LightGBMModel">LightGBMModel</a>,</p></td>
+<tr class="row-even"><td><p><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.lgbm.html#darts.models.forecasting.lgbm.LightGBMModel">LightGBMModel</a></p></td>
 <td></td>
 <td><p>🟩 🟩</p></td>
 <td><p>🟩 🟩 🟩</p></td>
diff --git a/objects.inv b/objects.inv
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diff --git a/py-modindex.html b/py-modindex.html
index 823a276528..f11325e8ec 100644
--- a/py-modindex.html
+++ b/py-modindex.html
@@ -564,11 +564,6 @@ <h1>Python Module Index</h1>
        <td>&#160;&#160;&#160;
        <a href="generated_api/darts.models.forecasting.baselines.html#module-darts.models.forecasting.baselines"><code class="xref">darts.models.forecasting.baselines</code></a></td><td>
        <em></em></td></tr>
-     <tr class="cg-1">
-       <td></td>
-       <td>&#160;&#160;&#160;
-       <a href="generated_api/darts.models.forecasting.block_rnn_model.html#module-darts.models.forecasting.block_rnn_model"><code class="xref">darts.models.forecasting.block_rnn_model</code></a></td><td>
-       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
@@ -659,11 +654,6 @@ <h1>Python Module Index</h1>
        <td>&#160;&#160;&#160;
        <a href="generated_api/darts.models.forecasting.regression_model.html#module-darts.models.forecasting.regression_model"><code class="xref">darts.models.forecasting.regression_model</code></a></td><td>
        <em></em></td></tr>
-     <tr class="cg-1">
-       <td></td>
-       <td>&#160;&#160;&#160;
-       <a href="generated_api/darts.models.forecasting.rnn_model.html#module-darts.models.forecasting.rnn_model"><code class="xref">darts.models.forecasting.rnn_model</code></a></td><td>
-       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
diff --git a/quickstart/00-quickstart.html b/quickstart/00-quickstart.html
index 465d0b1a5e..1d51c2ee3f 100644
--- a/quickstart/00-quickstart.html
+++ b/quickstart/00-quickstart.html
@@ -723,7 +723,7 @@ <h2>Some <code class="docutils literal notranslate"><span class="pre">TimeSeries
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">series1</span><span class="p">,</span> <span class="n">series2</span> <span class="o">=</span> <span class="n">series</span><span class="o">.</span><span class="n">split_before</span><span class="p">(</span><span class="mf">0.75</span><span class="p">)</span>
+<span></span><span class="n">series1</span><span class="p">,</span> <span class="n">series2</span> <span class="o">=</span> <span class="n">series</span><span class="o">.</span><span class="n">split_after</span><span class="p">(</span><span class="mf">0.75</span><span class="p">)</span>
 <span class="n">series1</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
 <span class="n">series2</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
 </pre></div>
diff --git a/quickstart/00-quickstart.ipynb b/quickstart/00-quickstart.ipynb
index 027ceb95e7..c4bf8a58f6 100644
--- a/quickstart/00-quickstart.ipynb
+++ b/quickstart/00-quickstart.ipynb
@@ -134,7 +134,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -146,7 +146,7 @@
     }
    ],
    "source": [
-    "series1, series2 = series.split_before(0.75)\n",
+    "series1, series2 = series.split_after(0.75)\n",
     "series1.plot()\n",
     "series2.plot()"
    ]
@@ -2970,7 +2970,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.10.13"
   },
   "pycharm": {
    "stem_cell": {
diff --git a/searchindex.js b/searchindex.js
index b7a8e2982c..5593a232ff 100644
--- a/searchindex.js
+++ b/searchindex.js
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