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 bytrim_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 iftrim==TRUE
.- parallel
(
logical(1)
)
IfTRUE
, 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. Usena.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.
See also
Other fda_featextractor:
extractFDABsignal()
,
extractFDADTWKernel()
,
extractFDAFPCA()
,
extractFDAFourier()
,
extractFDAMultiResFeatures()
,
extractFDAWavelets()