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Identification of equivalent dynamics using ordinal pattern distributions

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  • Applications to Real World Time Series
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Abstract

The frequency of occurrence of ordinal patterns in an observed (or measured) times series can be used to identify equivalent dynamical system. We demonstrate this approach for system identification and parameter estimation for dynamics that can (at least approximately) be described by one-dimensional iterated maps.

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Authors and Affiliations

  1. Max-Planck-Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077, Göttingen, Germany

    U. Parlitz & S. Luther

  2. Institute for Nonlinear Dynamics, Georg-August-Universität Göttingen, Am Faßberg 17, 37077, Göttingen, Germany

    U. Parlitz & S. Luther

  3. Department of Physics and Astronomy, Graduate School of Science and Engineering, Kagoshima University, 1-21-35 Korimto Kagoshima, 890-0065, Japan

    H. Suetani

  4. Decoding and Controlling Brain Information, PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, 332-0012, Japan

    H. Suetani

  5. Flucto-Order Functions Research Team, RIKEN–HYU Collaboration Research Center, 2-1 Hirosawa, Wako 351-0198, Tokyo, Japan

    H. Suetani

Authors
  1. U. Parlitz
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  2. H. Suetani
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  3. S. Luther
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Corresponding author

Correspondence to U. Parlitz.

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Parlitz, U., Suetani, H. & Luther, S. Identification of equivalent dynamics using ordinal pattern distributions. Eur. Phys. J. Spec. Top. 222, 553–568 (2013). https://doi.org/10.1140/epjst/e2013-01859-2

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  • DOI: https://doi.org/10.1140/epjst/e2013-01859-2

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