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What information is hidden in chaotic time series?

In: Practical Fruits of Econophysics

Author

Listed:
  • Serge F. Timashev

    (Karpov Institute of Physical Chemistry)

  • Grigory V. Vstovsky

    (Russian Academy of Science)

  • Anna B. Solovieva

    (Russian Academy of Science)

Abstract

Summary Foundations of Flicker-Noise Spectroscopy (FNS) which is a new phenomenological approach to extract information hidden in chaotic signals are presented. The information is formed by sequences of distinguished types of signal irregularities — spikes, jumps, and discontinuities of derivatives of different orders — at all space-time hierarchical levels of systems. The ability to distinguish irregularities means that parameters or patterns characterizing the totality of properties of the irregularities are distinguishably extracted from the power spectra S(f) (f — frequency) and difference moments Ф(p)(τ) (τ — temporal delay) of the p th order. It is shown that FNS method can be used to solve the problems of two types: to show of the parameters characterizing dynamics and peculiarities of structural organization of open complex systems; to reveal the precursors of the sharpest changes in the states of open dissipative systems of various nature on the base of a priori information about their dynamics. Applications of the FNS for getting information hidden in economical data (daily market prices for the Nasdaq- and Nikkei-Index time series) are presented.

Suggested Citation

  • Serge F. Timashev & Grigory V. Vstovsky & Anna B. Solovieva, 2006. "What information is hidden in chaotic time series?," Springer Books, in: Hideki Takayasu (ed.), Practical Fruits of Econophysics, pages 168-172, Springer.
    • Handle: RePEc:spr:sprchp:978-4-431-28915-9_30
    DOI: 10.1007/4-431-28915-1_30
    as

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