calculate_features: Compute features on an input time series dataset

In theft: Tools for Handling Extraction of Features from Time Series

Compute features on an input time series dataset

Description

Compute features on an input time series dataset

Usage

calculate_features(
  data,
  id_var = "id",
  time_var = "timepoint",
  values_var = "values",
  group_var = NULL,
  feature_set = c("catch22", "feasts", "tsfeatures", "Kats", "tsfresh", "TSFEL"),
  catch24 = FALSE,
  tsfresh_cleanup = FALSE,
  features = NULL,
  seed = 123
)

Arguments

data	data.frame with at least 4 columns: id variable, group variable, time variable, value variable
id_var	character specifying the ID variable to identify each time series. Defaults to "id"
time_var	character specifying the time index variable. Defaults to "timepoint"
values_var	character specifying the values variable. Defaults to "values"
group_var	character specifying the grouping variable that each unique series sits under (if one exists). Defaults to NULL
feature_set	character or vector of character denoting the set of time-series features to calculate. Defaults to "catch22"
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
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
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, 
  id_var = "id", 
  time_var = "timepoint", 
  values_var = "values", 
  group_var = "process", 
  feature_set = "catch22",
  seed = 123)

theft documentation built on Oct. 4, 2024, 1:08 a.m.