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.. _glossary: | ||
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Glossary | ||
======== | ||
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This page lists some common terms used in documentation of the library. | ||
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.. glossary:: | ||
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Time series | ||
A series of variable measurements obtained at successive times according to :term:`frequency <time series frequency>`. | ||
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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. | ||
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Univariate time series | ||
A single :term:`time series` containing measurements of a scalar variable. | ||
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Multivariate time series | ||
A single :term:`time series` containing measurements of a multidimensional variable. | ||
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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. | ||
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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`. | ||
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Segment | ||
We use this term to refer to one :term:`time series` in a :term:`dataset`. | ||
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Endogenous data | ||
Variables which measurements we want to model. It is often referred to as the "target". | ||
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Exogenous data | ||
Additional variables in a dataset that help to model :term:`target <endogenous data>`. | ||
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Regressor | ||
:term:`Exogenous variable <exogenous data>` whose values are known in the future during :term:`forecasting`. | ||
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Stationarity | ||
Property of a time series to retain its statistical properties over time. | ||
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Seasonality | ||
Property of time series to have a seasonal pattern of some fixed length. | ||
For example, weekly pattern for daily time series. | ||
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Trend | ||
Property of time series to have a long-term change of the mean value. | ||
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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. | ||
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Forecasting | ||
The task of predicting future values of a time series. | ||
We are only interested in forecasting :term:`target <endogenous data>` variables. | ||
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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. | ||
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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. | ||
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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. | ||
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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. | ||
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Backtesting | ||
Type of cross-validation when we check the quality of the forecast model using historical data. | ||
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Per-segment / local approach | ||
Mode of operation when there is a separate :term:`model` / :term:`transform` for each :term:`segment` of the dataset. | ||
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Multi-segment / Global approach | ||
Mode of operation when there is one :term:`model` / :term:`transform` for every :term:`segment` of the dataset. | ||
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Forecasting strategy | ||
Algorithm for using an ML model to produce a multi-step time series :term:`forecast <forecasting>`. | ||
See :doc:`tutorials/09-forecasting_strategies`. | ||
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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". | ||
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Clustering | ||
The task of finding clusters of similar time series. | ||
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Classification | ||
The task of predicting a categorical label for the whole time series. | ||
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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`. | ||
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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`. | ||
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Model | ||
Entity for learning time series patterns to make a :term:`forecast <forecasting>`. See :doc:`api_reference/models`. | ||
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Transform | ||
Entity for performing transformations on a :term:`dataset`. See :doc:`api_reference/transforms`. | ||
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Pipeline | ||
High-level entity for solving :term:`forecasting` task. Works with :term:`dataset`, :term:`model`, :term:`transforms <transform>` and other :term:`pipelines <pipeline>`. | ||
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Lags | ||
The features generated by :py:class:`~etna.transforms.math.lags.LagTransform`. | ||
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Date flags | ||
The features generated by :py:class:`~etna.transforms.timestamp.date_flags.DateFlagsTransform`. | ||
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Fourier terms | ||
The features generated by :py:class:`~etna.transforms.timestamp.fourier.FourierTransform`. | ||
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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`. |
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installation | ||
tutorials | ||
glossary | ||
resources |