R: Time-Series Feature Heuristics

extractFDATsfeatures {mlr} | R Documentation

## 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()`

[Package mlr version 2.17.1 Index]