iaawt: Iterated Amplitude Adjusted Wavelet Transform

In wol-fi/multifractal: Multifractal and Surrogate Analysis

Iterated Amplitude Adjusted Wavelet Transform

Description

Performs the iterated amplitude adjusted Wavelet transformation (IAAWT) upon a time-series x. The algorithm was translated from Chris Keylock's Matlab code into R.

Usage

iaawt(x, xdist=x, N=1, ...)

Arguments

x	a numeric vector containing the values of the time-series.
xdist	a numeric vector containing the values of the distribution to match. Default is set to xdist=x.
N	number of surrogates to create. Default is 1.
...	other parameters to control for the accuracy.

Details

The algorithm can be used to randomize a time-series x phase while keeping the non-linear correlation structure (point-wise Hölder regularity). It can further transform x to match any empirical distribution xdist.

Value

Returns a numeric vector or matrix of the surrogated time-series.

Note

Translated from Matlab into R. Original Matlab code by Chris Keylock.

Author(s)

Wolfgang Schadner

References

Keylock, C. J. (2017), Multifractal surrogate-data generation algorithm that preserves pointwise Holder regularity structure, with initial applications to turbulence, Physical Review E, 95(3), 032123.

See Also

iaaft

Examples

  n <- 1000
  # random normal variable with positive auto-correlation:
  x <- sort(rnorm(n))
  # random variable from t-distribution:
  y <- rt(n, 5)
  z <- iaawt(x, y)

wol-fi/multifractal documentation built on May 31, 2022, 1:18 a.m.