Update documentation with new tutorial about working with misaligned …

…data (#288)

CHANGELOG.md

@@ -29,7 +29,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - 
 - 
 - 
-- 
+- Add tutorial about working with misaligned data ([#288](https://github.com/etna-team/etna/pull/288))
 - 
 - 
 - 
@@ -46,7 +46,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - 
 - 
 - 
-- 
+- Update glossary with terms related to working with misaligned data ([#288](https://github.com/etna-team/etna/pull/288))
 - 
 - Add ignoring of integer timestamp as a feature into native DL models ([#210](https://github.com/etna-team/etna/pull/210))
 - Update `pytorch_forecasting` models to handle integer timestamp ([#208](https://github.com/etna-team/etna/pull/208))

README.md

@@ -192,9 +192,10 @@ We have also prepared a set of tutorials for an easy introduction:
 | [Custom model and transform](https://github.com/etna-team/etna/tree/master/examples/301-custom_transform_and_model.ipynb)   | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/etna-team/etna/master?filepath=examples/301-custom_transform_and_model.ipynb) |
 | [Inference: using saved pipeline on a new data](https://github.com/etna-team/etna/tree/master/examples/302-inference.ipynb) |                  [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/etna-team/etna/master?filepath=examples/302-inference.ipynb) |
 | [Hierarchical time series](https://github.com/etna-team/etna/blob/master/examples/303-hierarchical_pipeline.ipynb)          |      [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/etna-team/etna/master?filepath=examples/303-hierarchical_pipeline.ipynb) |
-| [Forecast interpretation](https://github.com/etna-team/etna/tree/master/examples/304-forecasting_interpretation.ipynb)         |    [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/etna-team/etna/master?filepath=examples/304-forecasting_interpretation.ipynb) |
+| [Forecast interpretation](https://github.com/etna-team/etna/tree/master/examples/304-forecasting_interpretation.ipynb)      |    [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/etna-team/etna/master?filepath=examples/304-forecasting_interpretation.ipynb) |
 | [Classification](https://github.com/etna-team/etna/blob/master/examples/305-classification.ipynb)                           |             [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/etna-team/etna/master?filepath=examples/305-classification.ipynb) |
 | [Prediction intervals](https://github.com/etna-team/etna/tree/master/examples/306-prediction_intervals.ipynb)               |       [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/etna-team/etna/master?filepath=examples/306-prediction_intervals.ipynb) |
+| [Working with misaligned data](https://github.com/etna-team/etna/tree/master/examples/307-working_with_misaligned_data.ipynb)       |       [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/etna-team/etna/master?filepath=examples/307-working_with_misaligned_data.ipynb) |
 
 ## Documentation
 

docs/source/api_reference/datasets.rst

@@ -42,3 +42,6 @@ Utilities for data manipulation:
 
    duplicate_data
    set_columns_wide
+   infer_alignment
+   apply_alignment
+   make_timestamp_df_from_alignment

docs/source/glossary.rst

@@ -10,10 +10,19 @@ This page lists some common terms used in documentation of the library.
    Time series
       A series of variable measurements obtained at successive times according to :term:`frequency <time series frequency>`.
 
-   Time series frequency
-      Quantity that determines how often we take measurements for :term:`time series`.
+   Timestamps
+      Times at which measurements are taken for :term:`time series`.
+
+   Regular timestamps
+      :term:`Timestamps` that are spaced regularly, for example every hour.
       It doesn't have to be always the same number of seconds.
-      For example, taking the first day of each month is a valid frequency.
+      For example, taking the first day of each month gives regular timestamps.
+
+   Irregular timestamps
+      :term:`Timestamps` that aren't spaced regularly, for example it can be times at which our backend server receives a request.
+
+   Time series frequency
+      Quantity that determines the size of spaces between :term:`regular timestamps`. Examples of frequencies: hourly, daily, monthly.
 
    Univariate time series
       A single :term:`time series` containing measurements of a scalar variable.
@@ -29,7 +38,14 @@ This page lists some common terms used in documentation of the library.
    Hierarchical time series
       Multiple :term:`time series` having a level structure in which higher levels can be disaggregated
       by different attributes of interest into series of lower levels.
-      See :doc:`tutorials/14-hierarchical_pipeline`.
+      See :doc:`tutorials/303-hierarchical_pipeline`.
+
+   Aligned time series
+      Set of :term:`time series` that have the same :term:`time series frequency` and end with the same :term:`timestamps`.
+
+   Misaligned time series
+      Set of :term:`time series` that have the same :term:`time series frequency`, but end with different :term:`timestamps`.
+      These times series can be shifted in time to become aligned.
 
    Segment
       We use this term to refer to one :term:`time series` in a :term:`dataset`.
@@ -84,15 +100,15 @@ This page lists some common terms used in documentation of the library.
    Backtesting
       Type of cross-validation when we check the quality of the forecast model using historical data.
 
-   Per-segment / local approach
+   Per-segment / Local approach
       Mode of operation when there is a separate :term:`model` / :term:`transform` for each :term:`segment` of the dataset.
 
    Multi-segment / Global approach
       Mode of operation when there is one :term:`model` / :term:`transform` for every :term:`segment` of the dataset.
 
    Forecasting strategy
       Algorithm for using an ML model to produce a multi-step time series :term:`forecast <forecasting>`.
-      See :doc:`tutorials/09-forecasting_strategies`.
+      See :doc:`tutorials/208-forecasting_strategies`.
 
    Forecasting context
       Suffix of a :term:`dataset` we want to :term:`forecast <forecasting>` that is necessary for the :term:`model` we are using.

docs/source/tutorials.rst

@@ -184,3 +184,12 @@ Advanced
 
       ^^^
       How to estimate prediction intervals
+
+   .. grid-item-card:: Working with misaligned data
+      :text-align: center
+      :link: tutorials/307-working_with_misaligned_data
+      :link-type: doc
+      :class-header: card-tutorial-advanced
+
+      ^^^
+      How to work with misaligned data

etna/datasets/__init__.py

@@ -9,5 +9,8 @@
 from etna.datasets.internal_datasets import load_dataset
 from etna.datasets.tsdataset import TSDataset
 from etna.datasets.utils import DataFrameFormat
+from etna.datasets.utils import apply_alignment
 from etna.datasets.utils import duplicate_data
+from etna.datasets.utils import infer_alignment
+from etna.datasets.utils import make_timestamp_df_from_alignment
 from etna.datasets.utils import set_columns_wide

examples/README.md

@@ -118,7 +118,6 @@ We have prepared a set of tutorials for an easy introduction:
   - `DirectEnsemble`
 - Summary
 
-
 #### [Mechanics of forecasting](https://github.com/etna-team/etna/tree/master/examples/209-mechanics_of_forecasting.ipynb)
 - Loading dataset
 - Forecasting
@@ -180,7 +179,6 @@ We have prepared a set of tutorials for an easy introduction:
   - Loading pretrained analyzer 
   - Analyzing segments predictability
 
-
 #### [Prediction intervals](https://github.com/etna-team/etna/tree/master/examples/306-prediction_intervals.ipynb)
 - Loading and preparing data
 - Estimating intervals using builtin method
@@ -195,6 +193,18 @@ We have prepared a set of tutorials for an easy introduction:
     - Non-parametric method
     - Estimating historical residuals
 
+#### [Working with misaligned data](https://github.com/etna-team/etna/tree/master/examples/307-working_with_misaligned_data.ipynb)
+- Loading data
+- Preparing data
+    - Using `TSDataset.create_from_misaligned`
+    - Using `infer_alignment`
+    - Using `apply_alignment`
+    - Using `make_timestamp_df_from_alignment`
+- Examples with regular data
+    - Forecasting with `CatBoostMultiSegmentModel`
+    - Utilizing old data with `CatBoostMultiSegmentModel`
+    - Forecasting with `ProphetModel`
+- Working with irregular data 
 
 ## Scripts