Create documentation page with glossary of terms

CHANGELOG.md

@@ -14,8 +14,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - 
 - Implementation of PatchTS model ([#1277](https://github.com/tinkoff-ai/etna/pull/1277))
 - Add `quickstart` notebook, add `mechanics_of_forecasting` notebook ([#1343](https://github.com/tinkoff-ai/etna/pull/1343))
-- Add gallery of tutorials divided by level ([#1354](https://github.com/tinkoff-ai/etna/pull/1354))
-- Create documentation page with links to external resources ([#1350](https://github.com/tinkoff-ai/etna/pull/1350))
+- Add gallery of tutorials divided by level ([#46](https://github.com/etna-team/etna/pull/46))
+- Create documentation page with links to external resources ([#44](https://github.com/etna-team/etna/pull/44))
+- Add documentation page with glossary of terms ([#45](https://github.com/etna-team/etna/pull/45/))
 
 ### Changed
 - 

docs/source/api_reference.rst

@@ -3,7 +3,7 @@ API Reference
 
 In this section you can examine the interfaces of the :code:`etna` library.
 Here we describe the API of available modules, classes and functions.
-For more user-friendly manual please use :ref:`tutorials` section.
+For more user-friendly manual please use :doc:`tutorials` section.
 
 .. toctree::
    :titlesonly:

docs/source/glossary.rst

@@ -0,0 +1,137 @@
+.. _glossary:
+
+Glossary
+========
+
+This page lists some common terms used in documentation of the library.
+
+.. glossary::
+
+   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`.
+      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.
+
+   Univariate time series
+      A single :term:`time series` containing measurements of a scalar variable.
+
+   Multivariate time series
+      A single :term:`time series` containing measurements of a multidimensional variable.
+
+   Panel time series
+      Multiple :term:`time series`. It is closely related to :term:`multivariate time series`,
+      but the second term is usually used when the components are closely related,
+      and it is more useful to treat them as a single multidimensional value.
+
+   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`.
+
+   Segment
+      We use this term to refer to one :term:`time series` in a :term:`dataset`.
+
+   Endogenous data
+      Variables which measurements we want to model. It is often referred to as the "target".
+
+   Exogenous data
+      Additional variables in a dataset that help to model :term:`target <endogenous data>`.
+
+   Regressor
+      :term:`Exogenous variable <exogenous data>` whose values are known in the future during :term:`forecasting`.
+
+   Stationarity
+      Property of a time series to retain its statistical properties over time.
+
+   Seasonality
+      Property of time series to have a seasonal pattern of some fixed length.
+      For example, weekly pattern for daily time series.
+
+   Trend
+      Property of time series to have a long-term change of the mean value.
+
+   Change-point
+      Point in a time series where its behavior changes.
+      Its existence is the reason why you shouldn't trust your long-term forecasts too much.
+
+   Forecasting
+      The task of predicting future values of a time series.
+      We are only interested in forecasting :term:`target <endogenous data>` variables.
+
+   Forecasting horizon
+      Set of time points we are going to :term:`forecast <forecasting>`. Often it is set to a fixed value.
+      For example, horizon is equal to 7 if we want to make a forecast on 7 time points ahead for daily time series.
+
+   Forecast confidence intervals
+      Confidence intervals for the :math:`\mathop{E}(y | X)`.
+      Set of intervals for every point in the :term:`horizon <forecasting horizon>` can be called a confidence band.
+      Often confused with :term:`prediction intervals <forecast prediction intervals>`,
+      see `The difference between prediction intervals and confidence intervals <https://robjhyndman.com/hyndsight/intervals/>`_ to understand the difference.
+
+   Forecast prediction intervals
+      Prediction intervals for predicted random variables.
+      Set of intervals for every point in the :term:`horizon <forecasting horizon>` can be called a prediction band.
+      Often confused with :term:`confidence intervals <forecast confidence intervals>`,
+      see `The difference between prediction intervals and confidence intervals <https://robjhyndman.com/hyndsight/intervals/>`_ to understand the difference.
+
+   Forecast prediction components
+      In forecast decomposition each point is represented as the sum or product of some fixed terms. These terms are called components.
+      We are currently working only with additive components.
+
+   Backtesting
+      Type of cross-validation when we check the quality of the forecast model using historical data.
+
+   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`.
+
+   Forecasting context
+      Suffix of a :term:`dataset` we want to :term:`forecast <forecasting>` that is necessary for the :term:`model` we are using.
+      Can be also be referred to as the "model context".
+
+   Clustering
+      The task of finding clusters of similar time series.
+
+   Classification
+      The task of predicting a categorical label for the whole time series.
+
+   Segmentation
+      The task of dividing each time series into sequence of intervals with different characteristics.
+      These intervals are separated by :term:`change-points <change-point>`.
+      This shouldn't be confused with the term :term:`segment`.
+
+   Dataset
+      Collection of time series to work with.
+      In the context of the library this is often used to refer to :py:class:`~etna.datasets.tsdataset.TSDataset`.
+
+   Model
+      Entity for learning time series patterns to make a :term:`forecast <forecasting>`. See :doc:`api_reference/models`.
+
+   Transform
+      Entity for performing transformations on a :term:`dataset`. See :doc:`api_reference/transforms`.
+
+   Pipeline
+      High-level entity for solving :term:`forecasting` task. Works with :term:`dataset`, :term:`model`, :term:`transforms <transform>` and other :term:`pipelines <pipeline>`.
+
+   Lags
+      The features generated by :py:class:`~etna.transforms.math.lags.LagTransform`.
+
+   Date flags
+      The features generated by :py:class:`~etna.transforms.timestamp.date_flags.DateFlagsTransform`.
+
+   Fourier terms
+      The features generated by :py:class:`~etna.transforms.timestamp.fourier.FourierTransform`.
+
+   Differencing
+      Time series :term:`transformation <transform>` that takes the differences between consecutive time points.
+      There is also a seasonal differencing with period :math:`p`, where we take the difference between the current point and its :term:`lag <lags>` of order :math:`p`.
+      See :py:class:`~etna.transforms.math.differencing.DifferencingTransform`.

docs/source/user_guide.rst

@@ -12,4 +12,5 @@ Contents
 
    installation
    tutorials
+   glossary
    resources