Tools for Handling Extraction of Features from Time series (theft)
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.
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:
Rcatch22 for the native implementation on CRAN)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.
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 0.5.4.1,
<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 0.5.4.1},
url = {https://hendersontrent.github.io/theft/},
}