Bivariate surrogate techniques: Necessity, strengths, and caveats

Ralph G. Andrzejak, Alexander Kraskov, Harald Stögbauer, Florian Mormann, and Thomas Kreuz
Phys. Rev. E 68, 066202 – Published 15 December 2003
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Abstract

The concept of surrogates allows testing results from time series analysis against specified null hypotheses. In application to bivariate model dynamics we here compare different types of surrogates, each designed to test against a different null hypothesis, e.g., an underlying bivariate linear stochastic process. Two measures that aim at a characterization of interdependence between nonlinear deterministic dynamics were used as discriminating statistics. We analyze eight different stochastic and deterministic models not only to demonstrate the power of the surrogates, but also to reveal some pitfalls and limitations.

  • Received 28 May 2003

DOI:https://doi.org/10.1103/PhysRevE.68.066202

©2003 American Physical Society

Authors & Affiliations

Ralph G. Andrzejak1,*, Alexander Kraskov1, Harald Stögbauer1, Florian Mormann2, and Thomas Kreuz1,2

  • 1John-von-Neumann Institute for Computing, Forschungszentrum Jülich, 52425 Jülich, Germany
  • 2Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn, Germany

  • *Electronic address: r.g.andrzejak@fz-juelich.de

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Vol. 68, Iss. 6 — December 2003

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