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Effect of the signal filtering on detrended fluctuation analysis

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  • Li, Ruixue
  • Wang, Jiang
  • Chen, Yingyuan

Abstract

Detrended fluctuation analysis (DFA) is an effective method to accurately quantify long-term correlations embedded in a nonstationary time series. In this paper, we study the effect of signal filtering of a signal on the DFA method. The theoretical and simulated results show that the signal filtering will affect the range of scale in DFA. Moreover, this effect is different for fractal Gaussian noise series and fractal Brown movement series. Our study is meaningful for improving accuracy and efficiency of DFA method in theory and practice.

Suggested Citation

  • Li, Ruixue & Wang, Jiang & Chen, Yingyuan, 2018. "Effect of the signal filtering on detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 446-453.
    • Handle: RePEc:eee:phsmap:v:494:y:2018:i:c:p:446-453
    DOI: 10.1016/j.physa.2017.12.011
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    1. Hassler, Uwe & Wolters, Jurgen, 1995. "Long Memory in Inflation Rates: International Evidence," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 37-45, January.
    2. Sánchez Granero, M.A. & Trinidad Segovia, J.E. & García Pérez, J., 2008. "Some comments on Hurst exponent and the long memory processes on capital markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(22), pages 5543-5551.
    3. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    4. Hu, Kun & Ivanov, Plamen Ch. & Chen, Zhi & Hilton, Michael F. & Stanley, H.Eugene & Shea, Steven A., 2004. "Non-random fluctuations and multi-scale dynamics regulation of human activity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 337(1), pages 307-318.
    5. Kantelhardt, Jan W & Koscielny-Bunde, Eva & Rego, Henio H.A & Havlin, Shlomo & Bunde, Armin, 2001. "Detecting long-range correlations with detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 295(3), pages 441-454.
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