Help for package theft

Type:

Package

Title:

Tools for Handling Extraction of Features from Time Series

Version:

0.8.2

Date:

2025-07-29

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:

Packaged:

2025-07-29 08:42:59 UTC; trenthenderson

Author:

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

Repository:

CRAN

Date/Publication:

2025-07-29 10:40:02 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",
    "quantiles", "moments"),
  features = NULL,
  catch24 = FALSE,
  tsfresh_cleanup = FALSE,
  use_compengine = FALSE,
  seed = 123,
  z_score = FALSE,
  n_jobs = 0,
  warn = TRUE
)

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", "kats", "quantiles", and or "moments"

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

z_score

Boolean specifying whether to z-score the time-series before computing features. Defaults to FALSE

n_jobs

integer denoting the number of parallel processes to use if "tsfresh" or "tsfel" are specified in "feature_set". Defaults to 0 for no parallelisation

warn

Boolean specifying whether to produce warnings from feature set packages. Defaults to TRUE

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)

Calculate a kurtosis of a vector

Description

Calculate a kurtosis of a vector

Usage

kurtosis(y)

Arguments

y

numeric vector of values

Value

numeric scalar of kurtosis

Author(s)

Trent Henderson

Calculate a basic set of the four moments of the distribution

Description

Calculate a basic set of the four moments of the distribution

Usage

moments(y)

Arguments

y

numeric vector of values

Value

data.frame of results

Author(s)

Trent Henderson

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

Calculate a basic set of quantiles for an input time-series vector

Description

Calculate a basic set of quantiles for an input time-series vector

Usage

quantiles(y, quantiles = seq(0.01, 1, by = 0.01))

Arguments

y

numeric vector of values

quantiles

numeric vector of quantiles to calculate. Defaults to seq(0.01, 1, by = 0.01)

Value

data.frame of results

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

Calculate a skewness of a vector

Description

Calculate a skewness of a vector

Usage

skewness(y)

Arguments

y

numeric vector of values

Value

numeric scalar of skewness

Author(s)

Trent Henderson

Tools for Handling Extraction of Features from Time-series

Description

Tools for Handling Extraction of Features from Time-series