Generating surrogate data for time series with several simultaneously measured variables

Details Generating surrogate data for time series with several simultaneously measured variables Generating surrogate data for time series with several simultaneously measured variables

1994-05-12

arxiv.org/abs/comp-gas/9405002v1

Nonlinear Sciences

Cellular Automata and Lattice Gases

4 pages, uuencoded compressed postscript file

Scientific paper

10.1103/PhysRevLett.73.951

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.

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