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:


You can install the development version of Rcatch22 from GitHub using the following:


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.

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.


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.

A BibTeX entry for LaTeX users is

    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:

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Rcatch22 documentation built on June 3, 2022, 9:08 a.m.