R package for the calculation of 22 CAnonical Time-series CHaracteristics. The package is an efficient implementation that calculates time-series features coded in C.
You can install the stable version of Rcatch22 from CRAN
using the following:
install.packages("Rcatch22")You can install the development version of Rcatch22 from
GitHub using the following:
devtools::install_github("hendersontrent/Rcatch22")You might also be interested in a related R package called theft
(Tools for Handling Extraction of Features from Time series) which
provides standardised access to Rcatch22 and 5 other
feature sets (including 3 feature sets from Python libraries) for a
total of ~1,200 features. theft also includes extensive
functionality for processing and analysing time-series features,
including automatic time-series classification, top performing feature
identification, and a range of statistical data visualisations.
Please open the included vignette within an R environment or visit
the detailed Rcatch22
Wiki for information and tutorials.
With features coded in C, Rcatch22 is highly
computationally efficient, scaling nearly linearly with time-series
size. Computation time in seconds for a range of time series lengths is
presented below.
An option to include the mean and standard deviation as features in
addition to catch22 is available through setting the
catch24 argument to TRUE:
features <- catch22_all(x, catch24 = TRUE)A DOI is provided at the top of this README. Alternatively, the package can be cited using the following:
To cite package 'Rcatch22' in publications use:
Trent Henderson (2022). Rcatch22: Calculation of 22 CAnonical
Time-Series CHaracteristics. R package version 0.2.1.
https://CRAN.R-project.org/package=Rcatch22
A BibTeX entry for LaTeX users is
@Manual{,
title = {Rcatch22: Calculation of 22 CAnonical Time-Series CHaracteristics},
author = {Trent Henderson},
year = {2022},
note = {R package version 0.2.1},
url = {https://CRAN.R-project.org/package=Rcatch22},
}Please also cite the original catch22 paper: