Type: Package
Title: Tools for Handling Extraction of Features from Time Series
Version: 0.8.1
Date: 2025-07-10
Maintainer: Trent Henderson <then6675@uni.sydney.edu.au>
Description: 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/.
BugReports: https://github.com/hendersontrent/theft/issues
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Depends: R (≥ 3.5.0)
Imports: utils, stats, rlang, dplyr, tidyr, purrr, tsibble, fabletools, feasts, tsfeatures, Rcatch22, reticulate, R.matlab
Suggests: lifecycle, cachem, bslib, knitr, rmarkdown, pkgdown, testthat
RoxygenNote: 7.3.2
VignetteBuilder: knitr
URL: https://hendersontrent.github.io/theft/
NeedsCompilation: no
Packaged: 2025-07-10 02:30:55 UTC; trenthenderson
Author: Trent Henderson [cre, aut], Annie Bryant [ctb]
Repository: CRAN
Date/Publication: 2025-07-10 07:10:02 UTC

Compute features on an input time series dataset

Description

Compute features on an input time series dataset

Usage

calculate_features(
  data,
  feature_set = c("catch22", "feasts", "tsfeatures", "kats", "tsfresh", "tsfel"),
  features = NULL,
  catch24 = FALSE,
  tsfresh_cleanup = FALSE,
  use_compengine = FALSE,
  seed = 123
)

Arguments

data

tbl_ts containing the time series data

feature_set

character or vector of character denoting the set of time-series features to calculate. Can be one of "catch22", "feasts", "tsfeatures", "tsfresh", "tsfel", or "kats"

features

named list containing a set of user-supplied functions to calculate on data. Each function should take a single argument which is the time series. Defaults to NULL for no manually-specified features. Each list entry must have a name as calculate_features looks for these to name the features. If you don't want to use the existing feature sets and only compute those passed to features, set feature_set = NULL

catch24

Boolean specifying whether to compute catch24 in addition to catch22 if catch22 is one of the feature sets selected. Defaults to FALSE

tsfresh_cleanup

Boolean specifying whether to use the in-built tsfresh relevant feature filter or not. Defaults to FALSE

use_compengine

Boolean specifying whether to use the "compengine" features in tsfeatures. Defaults to FALSE to provide immense computational efficiency benefits

seed

integer denoting a fixed number for R's random number generator to ensure reproducibility. Defaults to 123

Value

object of class feature_calculations that contains the summary statistics for each feature

Author(s)

Trent Henderson

Examples

featMat <- calculate_features(data = simData, 
  feature_set = "catch22")

Check for presence of NAs and non-numerics in a vector

Description

Check for presence of NAs and non-numerics in a vector

Usage

check_vector_quality(x)

Arguments

x

input vector

Value

Boolean of whether the data is good to extract features on or not

Author(s)

Trent Henderson

All features available in theft in tidy format

Description

The variables include:

Usage

feature_list

Format

A tidy data frame with 2 variables:

feature_set

Name of the set the feature is from

feature

Name of the feature

Communicate to R the Python virtual environment containing the relevant libraries for calculating features

Description

Communicate to R the Python virtual environment containing the relevant libraries for calculating features

Usage

init_theft(venv)

Arguments

venv

character specifying the name of the to the Python virtual environment where "tsfresh", "TSFEL", and/or "Kats" are installed

Value

no return value; called for side effects

Author(s)

Trent Henderson

Examples

## Not run: 
install_python_pkgs("theft-test")
init_theft("theft-test")

## End(Not run)

Download and install Kats from Python into a new virtual environment

Description

Download and install Kats from Python into a new virtual environment

Usage

install_kats(venv, python)

Arguments

venv

character specifying the name of the new virtual environment to create

python

character specifying the filepath to the Python interpreter to use. Python 3.10 is recommended

Value

no return value; called for side effects

Author(s)

Trent Henderson

Examples

## Not run: 
install_kats("theft-test", "/usr/local/bin/python3.10")

## End(Not run)

Download and install tsfresh, TSFEL, and Kats from Python into a new virtual environment

Description

Download and install tsfresh, TSFEL, and Kats from Python into a new virtual environment

Usage

install_python_pkgs(venv, python)

Arguments

venv

character specifying the name of the new virtual environment to create

python

character specifying the filepath to the Python interpreter to use. Python 3.10 is recommended

Value

no return value; called for side effects

Author(s)

Trent Henderson

Examples

## Not run: 
install_python_pkgs("theft-test", "/usr/local/bin/python3.10")

## End(Not run)

Download and install TSFEL from Python into a new virtual environment

Description

Download and install TSFEL from Python into a new virtual environment

Usage

install_tsfel(venv, python)

Arguments

venv

character specifying the name of the new virtual environment to create

python

character specifying the filepath to the Python interpreter to use. Python 3.10 is recommended

Value

no return value; called for side effects

Author(s)

Trent Henderson

Examples

## Not run: 
install_tsfel("theft-test", "/usr/local/bin/python3.10")

## End(Not run)

Download and install tsfresh from Python into a new virtual environment

Description

Download and install tsfresh from Python into a new virtual environment

Usage

install_tsfresh(venv, python)

Arguments

venv

character specifying the name of the new virtual environment to create

python

character specifying the filepath to the Python interpreter to use. Python 3.10 is recommended

Value

no return value; called for side effects

Author(s)

Trent Henderson

Examples

## Not run: 
install_tsfresh("theft-test", "/usr/local/bin/python3.10")

## End(Not run)

Load in hctsa formatted MATLAB files of time series data into a tidy format ready for feature extraction

Description

Load in hctsa formatted MATLAB files of time series data into a tidy format ready for feature extraction

Usage

process_hctsa_file(data)

Arguments

data

string specifying the filepath to the MATLAB file to parse

Value

an object of class data.frame in tidy format

Author(s)

Trent Henderson

Sample of randomly-generated time series to produce function tests and vignettes

Description

The variables include:

Usage

simData

Format

A tidy tsibble with 4 variables:

id

Unique identifier for the time series

timepoint

Time index

values

Value

process

Group label for the type of time series

Tools for Handling Extraction of Features from Time-series

Description

Tools for Handling Extraction of Features from Time-series