Generating surrogate data for time series with several simultaneously measured variables

Dean Prichard; James Theiler

doi:10.1103/PhysRevLett.73.951

Generating surrogate data for time series with several simultaneously measured variables

Dean Prichard and James Theiler

Phys. Rev. Lett. 73, 951 – Published 15 August 1994

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.

Received 28 March 1994

DOI:https://doi.org/10.1103/PhysRevLett.73.951

Authors & Affiliations

Dean Prichard

Department of Physics, University of Alaska, Fairbanks, Alaska 99775

James Theiler

Santa Fe Institute, 1660 Old Pecos Trail, Santa Fe, New Mexico
Center for Nonlinear Studies and Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545