cumol / theft

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

theft

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

Installation

You can install the stable version of theft from CRAN:

install.packages("theft")

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

devtools::install_github("hendersontrent/theft")

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:

• catch22 (R; see Rcatch22 for the native implementation on CRAN)
• feasts (R)
• tsfeatures (R)
• Kats (Python)
• tsfresh (Python)
• TSFEL (Python)

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:

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.

Citation

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 0.5.3,
  <https://hendersontrent.github.io/theft/>.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {theft: Tools for Handling Extraction of Features from Time Series},
    author = {Trent Henderson},
    year = {2023},
    note = {R package version 0.5.3},
    url = {https://hendersontrent.github.io/theft/},
  }