Generating surrogate data for time series with several simultaneously measured variables

Preprint

12 May 1994

preprint
Published by arXiv in arXiv

https://doi.org/10.48550/arXiv.comp-gas/9405002

Abstract

We propose an extension to multivariate time series of the phase-randomized Fourier-transform algorithm for generating surrogate data. Such surrogate data sets must mimic not only the autocorrelations of each of the variables in the original data set, they must mimic the cross-correlations {\em between} all the variables as well. The method is applied both to a simulated example (the three components of the Lorenz equations) and to data from a multichannel electroencephalogram.

Keywords

GENERATING SURROGATE DATA
SIMULTANEOUSLY MEASURED
MEASURED VARIABLES
FOURIER TRANSFORM
LORENZ EQUATIONS
SURROGATE DATA
MULTICHANNEL
TIME SERIES
ALGORITHM

All Related Versions

Version 1, 1994-05-12, ArXiv
Version 1, RePEc (Unconfirmed version)
Published version: Physical Review Letters, 73 (7), 951.

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