Package: theft 0.8.4

Trent Henderson

theft: Tools for Handling Extraction of Features from Time Series

Consolidates and calculates different sets of time-series features from multiple 'R' and 'Python' packages including 'Rcatch22' Henderson, T. (2021) <doi:10.5281/zenodo.5546815>, 'feasts' O'Hara-Wild, M., Hyndman, R., and Wang, E. (2021) <https://CRAN.R-project.org/package=feasts>, 'tsfeatures' Hyndman, R., Kang, Y., Montero-Manso, P., Talagala, T., Wang, E., Yang, Y., and O'Hara-Wild, M. (2020) <https://CRAN.R-project.org/package=tsfeatures>, 'tsfresh' Christ, M., Braun, N., Neuffer, J., and Kempa-Liehr A.W. (2018) <doi:10.1016/j.neucom.2018.03.067>, 'TSFEL' Barandas, M., et al. (2020) <doi:10.1016/j.softx.2020.100456>, and 'Kats' Facebook Infrastructure Data Science (2021) <https://facebookresearch.github.io/Kats/>.

Authors:Trent Henderson [cre, aut], Annie Bryant [ctb]

theft_0.8.4.tar.gz
theft_0.8.4.zip(r-4.7)theft_0.8.4.zip(r-4.6)theft_0.8.4.zip(r-4.5)
theft_0.8.4.tgz(r-4.6-any)theft_0.8.4.tgz(r-4.5-any)
theft_0.8.4.tar.gz(r-4.7-any)theft_0.8.4.tar.gz(r-4.6-any)
theft_0.8.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
theft/json (API)

# Install 'theft' in R:
install.packages('theft', repos = c('https://hendersontrent.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/hendersontrent/theft/issues

Pkgdown/docs site:https://hendersontrent.github.io

Datasets:
  • feature_list - All features available in theft in tidy format
  • simData - Sample of randomly-generated time series to produce function tests and vignettes

On CRAN:

Conda:

feature-extractionmachine-learningtime-series

7.49 score 43 stars 1 packages 48 scripts 582 downloads 14 exports 83 dependencies

Last updated from:5440cdc8be. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK208
source / vignettesOK220
linux-release-x86_64OK210
macos-release-arm64OK148
macos-oldrel-arm64OK211
windows-develOK182
windows-releaseOK145
windows-oldrelOK157
wasm-releaseOK143

Exports:calculate_featuresinit_theftinit_theft_hctsainit_theft_katsinit_theft_tsfelinit_theft_tsfreshinstall_katsinstall_pyhctsainstall_python_pkgsinstall_tsfelinstall_tsfreshmomentsprocess_hctsa_filequantiles

Dependencies:anytimeBHclicodetoolscolorspacecpp11curldigestdistributionaldplyrfabletoolsfarverfeastsforecastfracdifffurrrfuturegenericsggdistggplot2ggtimeglobalsgluegtablehereisobandjsonlitelabelinglatticelifecyclelistenvlmtestlubridatemagrittrMatrixnlmennetnumDerivparallellypillarpkgconfigpngprogressrpurrrquadprogquantmodR.matlabR.methodsS3R.ooR.utilsR6rappdirsRcatch22RColorBrewerRcppRcppArmadilloRcppRollRcppTOMLreticulaterlangrprojrootS7scalessliderstringistringrtibbletidyrtidyselecttimechangetimeDatetseriestsfeaturestsibbleTTRurcautf8vctrsviridisLitewarpwithrxtszoo

Introduction to theft

Trent Henderson

Rendered fromtheft.Rmdusingknitr::rmarkdownon May 14 2026.

Last update: 2025-12-17
Started: 2021-04-07

Readme and manuals

Help Manual

Help pageTopics
Compute features on an input time series datasetcalculate_features
Check for presence of NAs and non-numerics in a vectorcheck_vector_quality
All features available in theft in tidy formatfeature_list
Communicate to R the Python virtual environment containing the relevant libraries for calculating featuresinit_theft
Communicate to R the Python virtual environment containing pyhctsa onlyinit_theft_hctsa
Communicate to R the Python virtual environment containing kats onlyinit_theft_kats
Communicate to R the Python virtual environment containing tsfel onlyinit_theft_tsfel
Communicate to R the Python virtual environment containing tsfresh onlyinit_theft_tsfresh
Download and install Kats from Python into a new virtual environmentinstall_kats
Download and install pyhctsa from Python into a new virtual environmentinstall_pyhctsa
Download and install tsfresh, TSFEL, and Kats from Python into a new virtual environmentinstall_python_pkgs
Download and install TSFEL from Python into a new virtual environmentinstall_tsfel
Download and install tsfresh from Python into a new virtual environmentinstall_tsfresh
Calculate a kurtosis of a vectorkurtosis
Calculate a basic set of the four moments of the distributionmoments
Load in hctsa formatted MATLAB files of time series data into a tidy format ready for feature extractionprocess_hctsa_file
Calculate a basic set of quantiles for an input time-series vectorquantiles
Sample of randomly-generated time series to produce function tests and vignettessimData
Calculate a skewness of a vectorskewness
Tools for Handling Extraction of Features from Time-seriestheft