Docs/quickstart update (#2418)

* improve quickstart notebook

* update readme

CHANGELOG.md

@@ -46,6 +46,9 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
   - 🔴 Removed parameter `num_loader_workers` from `fit*()`, `predict*()`, `find_lr()`. You can now set the parameter through the `dataloader_kwargs` dict.
 - Improvements to `DataTransformers`:
   - Significant speed up when using `fit`, `fit_transform`, `transform`, and `inverse_transform` with a large number of series. The component masking logic was moved into the parallelized transform methods. [#2401](https://github.com/unit8co/darts/pull/2401) by [Dennis Bader](https://github.com/dennisbader).
+- Improvements to quick start notebook : [#2418](https://github.com/unit8co/darts/pull/2418) by [Dennis Bader](https://github.com/dennisbader).
+  - Added examples for using sample weights, forecast start shifting, direct likelihood parameter predictions.
+  - Enhanced examples for historical forecasts, backtest and residuals.
 
 **Fixed**
 - Fixed a bug when using a `RegressionModel` (that supports validation series) with a validation set, and encoders and/or component-specific lags, where the encodings and component specific lags were not added to the set. [#2383](https://github.com/unit8co/darts/pull/2383) by [Dennis Bader](https://github.com/dennisbader).

README.md

@@ -164,7 +164,7 @@ series.plot()
   The `PyODScorer` makes it trivial to use PyOD detectors on time series.
 
 * **Multivariate Support:** `TimeSeries` can be multivariate - i.e., contain multiple time-varying
-  dimensions instead of a single scalar value. Many models can consume and produce multivariate series.
+  dimensions/columns instead of a single scalar value. Many models can consume and produce multivariate series.
 
 * **Multiple series training (global models):** All machine learning based models (incl. all neural networks)
   support being trained on multiple (potentially multivariate) series. This can scale to large datasets too.
@@ -186,6 +186,13 @@ series.plot()
 * **Regression Models:** It is possible to plug-in any scikit-learn compatible model
   to obtain forecasts as functions of lagged values of the target series and covariates.
 
+* **Training with sample weights:** All global models support being trained with sample weights. They can be
+  applied to each observation, forecasted time step and target column.
+
+* **Forecast Start Shifting:** All global models support training and prediction on a shifted output window.
+  This is useful for example for Day-Ahead Market forecasts, or when the covariates (or target series) are reported
+  with a delay.
+
 * **Explainability:** Darts has the ability to *explain* some forecasting models using Shap values.
 
 * **Data processing:** Tools to easily apply (and revert) common transformations on