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Superfamily classification of nonstationary time series based on DFA scaling exponents

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  • Chuang Liu

    (ECUST)

  • Wei-Xing Zhou

    (ECUST)

Abstract

The superfamily phenomenon of time series with different dynamics can be characterized by the motif rank patterns observed in the nearest-neighbor networks of the time series in phase space. However, the determinants of superfamily classification are unclear. We attack this problem by studying the influence of linear temporal correlations and multifractality using fractional Brownian motions (FBMs) and multifractal random walks (MRWs). Numerical investigations unveil that the classification of superfamily phenomenon is uniquely determined by the detrended fluctuation analysis (DFA) scaling exponent $\alpha$ of the time series. Only four motif patterns are observed in the simulated data, which are delimited by three DFA scaling exponents $\alpha \simeq 0.25$, $\alpha \simeq 0.35$ and $\alpha \simeq 0.45$. The validity of the result is confirmed by stock market indexes and turbulence velocity signals.

Suggested Citation

  • Chuang Liu & Wei-Xing Zhou, 2009. "Superfamily classification of nonstationary time series based on DFA scaling exponents," Papers 0912.2016, arXiv.org.
    • Handle: RePEc:arx:papers:0912.2016
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    References listed on IDEAS

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    1. Meng-Cen Qian & Zhi-Qiang Jiang & Wei-Xing Zhou, 2009. "Universal and nonuniversal allometric scaling behaviors in the visibility graphs of world stock market indices," Papers 0910.2524, arXiv.org.
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    Cited by:

    1. Caraiani, Petre, 2012. "Characterizing emerging European stock markets through complex networks: From local properties to self-similar characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(13), pages 3629-3637.

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