iaaft: Iterated Amplitude Adjusted Fourier Transform

In wol-fi/multifractal: Multifractal and Surrogate Analysis

Iterated Amplitude Adjusted Fourier Transform

Description

Performs the iterated amplitude adjusted Fourier transformation (IAAFT) upon a time-series x.

Usage

iaaft(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.
...	additional parameters to control for accuracy.

Details

The algorithm can be used to remove a time-series' x non-linear correlation structure (multi-fractality) while keeping the degree of linear correlation (persistence). It can further transform \itemx to match any empirical distribution xdist.

Value

Returns a numeric vector of the surrogated time-series.

Note

Code is based on Henning Rust (2006).

Author(s)

Wolfgang Schadner

References

T. Schreiber and A. Schmitz (2000), Surrogate time series, Physica D, 142, pp.346-382.

See Also

iaawt

Examples

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

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