Related Time-Series Resources

This reduced set of 22 features, determined through a combination of classification performance (across 93 problems) and mutual redundancy (as explained in ), is available as an efficiently coded C implementation.

A reduced set of 16 features (trained on mouse fMRI data), coded in C with wrappers for use in python, R, etc.

Native python time-series code to extract hundreds of time-series features, with in-built feature filtering. See the .

Kats is a toolkit for analysing time-series data in python, and includes basic feature extraction functionality.

TSFEL, 'Time Series Feature Extraction Library', is a python package with implementations of 60 simple time-series features (with unit tests).

A flexible and efficient toolkit for flexible time-series processing & feature extraction, that makes minimal assumptions about sequential data. Includes support for a range of feature sets, including tsfresh, catch22, TSFEL, and Kats.

Includes a wide range of useful signal processing tools (like power spectral densities, detrending and bandpass filtering, and empirical mode decomposition). It also includes estimation of complexity parameters (many entropies and correlation dimensions, see part of readme ) as well as detrended fluctuation analysis.

Makes available existing collections of time-series data for analysis.

Includes implementations of a range of time-series features.

Provides a collection of tools for the analysis of tidy time-series data represented as a .

A unified framework for machine learning with time series.

An open-source library of efficient algorithms to analyse time series using GPU and CPU.

A python-based nonlinear time-series analysis and complex systems code package. See this .

A python package for performing machine learning using time-series data, including loading datasets from the UCR/UAE repository.

A python package that can extract features from multivariate time series.