Time-Series Feature Heuristics

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,
  ...
)

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.

Arguments

Value

References

See also