CRAN version CRAN RStudio mirror downloads DOI

Tools for Handling Extraction of Features from Time series (theft)


You can install the stable version of theft from CRAN:


You can install the development version of theft from GitHub using the following:


Please also check out our paper Feature-Based Time-Series Analysis in R using the theft Package which discusses the motivation and theoretical underpinnings of theft and walks through all of its functionality using the Bonn EEG dataset — a well-studied neuroscience dataset.

General purpose

theft is a software package for R that facilitates user-friendly access to a structured analytical workflow for the extraction, analysis, and visualisation of time-series features. The package provides a single point of access to \(>1200\) time-series features from a range of existing R and Python packages. The packages which theft ‘steals’ features from currently are:

Note that Kats, tsfresh and TSFEL are Python packages. theft has built-in functionality for helping you install these libraries—all you need to do is install Python 3.9 on your machine. If you wish to access the Python feature sets, please run ?install_python_pkgs in R after downloading theft or consult the vignette in the package for more information. For a comprehensive comparison of these six feature sets across a range of domains (including computation speed, within-set feature composition, and between-set feature correlations), please refer to the paper An Empirical Evaluation of Time-Series Feature Sets.

The core workflow for feature-based time-series analysis (and corresponding functions) in theft is presented below:

Structured workflow of the theft package for R

As you can see from the graphic above, theft contains a convenient and extensive suite of tools for semi-automated processing of extracted features (including data quality assessments and normalisation methods), low dimensional projections (linear and nonlinear), data matrix and feature distribution visualisations, time-series classification procedures, statistical hypothesis testing, and various other statistical and graphical tools.


If you use theft in your own work, please cite both the paper:

T. Henderson and Ben D. Fulcher. Feature-Based Time-Series Analysis in R using the theft Package. arXiv, (2022).

and the software:

To cite package 'theft' in publications use:

  Henderson T (2023). _theft: Tools for Handling Extraction of Features
  from Time Series_. R package version,

A BibTeX entry for LaTeX users is

    title = {theft: Tools for Handling Extraction of Features from Time Series},
    author = {Trent Henderson},
    year = {2023},
    note = {R package version},
    url = {},