Generating surrogate data for time series with several simultaneously measured variables

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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 between all the variables as well. The method is applied both to a simulated example (the three components of the Lorentz equations) and to data from a multichannel electroencephalogram.