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Quantifying two-dimensional nonstationary signal with power-law correlations by detrended fluctuation analysis

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Abstract

In this paper, we develop a new method for the multifractal characterization of two-dimensional nonstationary signal, which is based on the detrended fluctuation analysis (DFA). By applying to two artificially generated signals of two-component ARFIMA process and binomial multifractal model, we show that the new method can reliably determine the multifractal scaling behavior of two-dimensional signal. We also illustrate the applications of this method in finance and physiology. The analyzing results exhibit that the two-dimensional signals under investigation are power-law correlations, and the electricity market consists of electricity price and trading volume is multifractal, while the two-dimensional EEG signal in sleep recorded for a single patient is weak multifractal. The new method based on the detrended fluctuation analysis may add diagnostic power to existing statistical methods.

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Authors and Affiliations

  1. Department of Statistics, School of Science, Wuhan University of Technology, Wuhan, 430070, P.R. China

    Qingju Fan & Yonghong Wu

Authors
  1. Qingju Fan
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  2. Yonghong Wu
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Correspondence to Qingju Fan.

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Fan, Q., Wu, Y. Quantifying two-dimensional nonstationary signal with power-law correlations by detrended fluctuation analysis. Eur. Phys. J. B 88, 199 (2015). https://doi.org/10.1140/epjb/e2015-60332-x

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  • DOI: https://doi.org/10.1140/epjb/e2015-60332-x

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