Time series transformations

The aeon.transformations module contains classes for data transformations.

All (simple) transformers in aeon``can be listed using the ``aeon.registry.all_estimators utility, using estimator_types="regressor", optionally filtered by tags. Valid tags can be listed using aeon.registry.all_tags.

For pairwise transformers (time series distances, kernels), instead see _transformations_pairwise_ref.

Transformations are categorized as follows:

Category	Explanation	Example
Composition	Building blocks for pipelines, wrappers, adapters	Transformer pipeline
Series-to-features	Transforms series to float/category vector	Length and mean
Series-to-series	Transforms individual series to series	Differencing, detrending
Series-to-Panel	transforms a series into a panel	Bootstrap, sliding window
Panel transform	Transforms panel to panel, not by-series	Padding to equal length
Hierarchical	uses hierarchy information non-trivially	Reconciliation

Composition

Pipeline building

Sklearn and pandas adapters

Series-to-features transformers

Series-to-features transformers transform individual time series to a collection of primitive features. Primitive features are usually a vector of floats, but can also be categorical.

When applied to panels or hierarchical data, the transformation result is a table with as many rows as time series in the collection.

Summarization

These transformers extract simple summary features.

Shapelets, wavelets, and convolution

Distance-based features

Dictionary-based features

Moment-based features

Feature collections

These transformers extract larger collections of features.

Series-to-series transformers

Series-to-series transformers transform individual time series into another time series.

When applied to panels or hierarchical data, individual series are transformed.

Lagging

Element-wise transforms

These transformations apply a function element-wise.

Depending on the transformer, the transformation parameters can be fitted.

Detrending

Filtering and denoising

Differencing and slope

Binning and segmentation

Missing value imputation

Seasonality and Date-Time Features

Auto-correlation series

Window-based series transforms

These transformers create a series based on a sequence of sliding windows.

Multivariate-to-univariate

These transformers convert multivariate series to univariate.

Augmentation

FeatureSelection

These transformers select features in X based on y.

Subsetting time points and variables

These transformers subset X by time points (pandas index or index level) or variables (pandas columns).

Panel transformers

Panel transformers transform a panel of time series into a panel of time series.

A panel transformer is fitted on an entire panel, and not per series.

Equal length transforms

These transformations ensure all series in a panel have equal length

Dimension reduction

Series-to-Panel transformers

These transformers create a panel from a single series.

Bootstrap transformations

Outlier detection, changepoint detection

Hierarchical transformers

These transformers are specifically for hierarchical data and panel data.

The transformation depends on the specified hierarchy in a non-trivial way.