What information is hidden in chaotic time series?

Serge F. Timashev, Grigory V. Vstovsky and Anna B. Solovieva
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Serge F. Timashev: Karpov Institute of Physical Chemistry
Grigory V. Vstovsky: Russian Academy of Science
Anna B. Solovieva: Russian Academy of Science

A chapter in Practical Fruits of Econophysics, 2006, pp 168-172 from Springer

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.

Keywords: Time series; flicker-noise spectroscopy; information; irregularities of dynamic variables; power spectrum; difference moment; non-stationary process; daily market prices (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-4-431-28915-9_30

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DOI: 10.1007/4-431-28915-1_30

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