extractFDATsfeatures: Time-Series Feature Heuristics

In mlr: Machine Learning in R

Time-Series Feature Heuristics

Description

The function extracts features from functional data based on known Heuristics. For more details refer to tsfeatures::tsfeatures(). Under the hood this function uses the package tsfeatures::tsfeatures(). For more information see Hyndman, Wang and Laptev, Large-Scale Unusual Time Series Detection, ICDM 2015.

Note: Currently computes the following features:
"frequency", "stl_features", "entropy", "acf_features", "arch_stat", "crossing_points", "flat_spots", "hurst", "holt_parameters", "lumpiness", "max_kl_shift", "max_var_shift", "max_level_shift", "stability", "nonlinearity"

Usage

extractFDATsfeatures(
  scale = TRUE,
  trim = FALSE,
  trim_amount = 0.1,
  parallel = FALSE,
  na.action = na.pass,
  feats = NULL,
  ...
)

Arguments

scale	(logical(1)) If TRUE, time series are scaled to mean 0 and sd 1 before features are computed.
trim	(logical(1)) If TRUE, time series are trimmed by trim_amount before features are computed. Values larger than trim_amount in absolute value are set to NA.
trim_amount	(numeric(1)) Default level of trimming if trim==TRUE.
parallel	(logical(1)) If TRUE, multiple cores (or multiple sessions) will be used. This only speeds things up when there are a large number of time series.
na.action	(logical(1)) A function to handle missing values. Use na.interp to estimate missing values
feats	(character) A character vector of function names to apply to each time-series in order to extract features. Default: feats = c("frequency", "stl_features", "entropy", "acf_features", "arch_stat", "crossing_points", "flat_spots", "hurst", "holt_parameters", "lumpiness", "max_kl_shift", "max_var_shift", "max_level_shift", "stability", "nonlinearity")
...	(any) Further arguments passed on to the respective tsfeatures functions.

Value

(data.frame)

References

Hyndman, Wang and Laptev, Large-Scale Unusual Time Series Detection, ICDM 2015.

See Also

Other fda_featextractor: extractFDABsignal(), extractFDADTWKernel(), extractFDAFPCA(), extractFDAFourier(), extractFDAMultiResFeatures(), extractFDAWavelets()

mlr documentation built on June 22, 2024, 10:51 a.m.