aaft: Amplitude Adjusted Fourier Transform (AAFT)

In agbarnett/season: Seasonal Analysis of Health Data

Amplitude Adjusted Fourier Transform (AAFT)

Description

Generates random linear surrogate data of a time series with the same second-order properties.

Usage

aaft(data, nsur)

Arguments

data	a vector of equally spaced numeric observations (time series).
nsur	the number of surrogates to generate (1 or more).

Details

The AAFT uses phase-scrambling to create a surrogate of the time series that has a similar spectrum (and hence similar second-order statistics). The AAFT is useful for testing for non-linearity in a time series, and is used by nonlintest.

Value

surrogates

a matrix of the nsur surrogates.

Author(s)

Adrian Barnett [e-mail]

References

Kugiumtzis D (2000) Surrogate data test for nonlinearity including monotonic transformations, Phys. Rev. E, vol 62

Examples

data(CVD)
surr = aaft(CVD$cvd, nsur=1)
plot(CVD$cvd, type='l')
lines(surr[,1], col='red')

agbarnett/season documentation built on March 26, 2022, 9:29 a.m.