emd: Automatic empirical mode decomposition

In RebeccaSalles/TSPred: Functions for Benchmarking Time Series Prediction

Description

The function automatically applies an empirical mode decomposition to a provided univariate time series. Wrapper function for emd of the Rlibeemd package. It also allows the automatic selection of meaningful IMFs using fittestEMD. emd.rev() reverses the transformation.

Usage

emd(
  x,
  num_imfs = 0,
  S_number = 4L,
  num_siftings = 50L,
  meaningfulImfs = NULL,
  h = 1,
  ...
)

emd.rev(pred)

Arguments

x	A numeric vector or univariate time series to be decomposed.
num_imfs	Number of Intrinsic Mode Functions (IMFs) to compute. See emd.
S_number, num_siftings	See emd.
meaningfulImfs	Vector indicating the indices of the meaningful IMFs according to the possible intervals i:num_imfs for i=1,...,(num_imfs-1), where num_imfs is the number of IMFs in a decomposition. If meaningfulImfs = NULL (default), the function returns all IMF's produced by emd as meaningful. If meaningfulImfs = 0 the function automatically selects the meaningful IMFs of a decomposition using fittestEMD.
h	See fittestEMD. Passed to fittestEMD if meaningfulImfs = 0.
...	Additional arguments passed to fittestEMD.
pred	A list containing IMFs produced by empirical mode decomposition.

Value

A list containing the meaningful IMFs of the empirical mode decomposition of x. A vector indicating the indices of the meaningful IMFs and the number of IMFs produced are passed as attributes named "meaningfulImfs" and "num_imfs", respectively.

Author(s)

Rebecca Pontes Salles

References

Kim, D., Paek, S. H., & Oh, H. S. (2008). A Hilbert-Huang transform approach for predicting cyber-attacks. Journal of the Korean Statistical Society, 37(3), 277-283.

See Also

fittestEMD, fittestWavelet

Other transformation methods: Diff(), LogT(), WaveletT(), mas(), mlm_io(), outliers_bp(), pct(), train_test_subset()

Examples

data(CATS)
e <- emd(CATS[,1])
x <- emd.rev(e)
all(round(x,4)==round(CATS[,1],4))

RebeccaSalles/TSPred documentation built on April 6, 2021, 2:44 a.m.