Calculation of 22 CAnonical Time-Series CHaracteristics.
Rcatch22
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.
Installation
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.
Wiki
Please open the included vignette within an R environment or visit the detailed Rcatch22
Wiki for information and tutorials.
Computational performance
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.
catch24
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)
Citation
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.
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},
}
Please also cite the original catch22 paper: