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"
extractFDATsfeatures(
scale = TRUE,
trim = FALSE,
trim_amount = 0.1,
parallel = FALSE,
na.action = na.pass,
feats = NULL,
...
)(logical(1))
If TRUE, time series are scaled to mean 0 and sd 1 before features are computed.
(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.
(numeric(1))
Default level of trimming if trim==TRUE.
(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.
(logical(1))
A function to handle missing values. Use na.interp to estimate missing values
(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.
Hyndman, Wang and Laptev, Large-Scale Unusual Time Series Detection, ICDM 2015.
Other fda_featextractor:
extractFDABsignal(),
extractFDADTWKernel(),
extractFDAFPCA(),
extractFDAFourier(),
extractFDAMultiResFeatures(),
extractFDAWavelets()