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Effect Of Filters On Multivariate Multifractal Detrended Fluctuation Analysis

Author

Listed:
  • QINGJU FAN

    (Department of Statistics, School of Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, P. R. China)

  • DAN LI

    (Department of Statistics, School of Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, P. R. China)

  • GUANG LING

    (Department of Statistics, School of Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, P. R. China)

  • FANG WANG

    (��College of Science, Agricultural Mathematical Modeling and Data Processing Center, Hunan Agricultural University, Changsha, 410128, P. R. China)

  • SHUANGGUI LIU

    (Department of Statistics, School of Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, P. R. China)

Abstract

We investigate how various linear and nonlinear filters affect the scaling properties of long-range power-law multivariate synthetic series quantified by multivariate multifractal detrended fluctuation analysis (MV-MFDFA). We consider four types of transforms which are often encountered in physical and physiological processes: linear, nonlinear polynomial, logarithmic and power-law filters. The effect of filters is analyzed by numerical simulation of synthetic series generated by ARFIMA process and binomial multifractal model. The representation of auto-correlation properties of synthetic series before and after the transforms is illustrated by 3D Hurst surface, and the difference of effect is quantified by the proposed generalized mean distance. We find that the linear filters do not change the scaling properties of both synthetic series, while the effect of nonlinear polynomial is correlated with the power of the polynomial filter. For logarithmic and exponential filter, the scaling behavior is not affected for some values of the parameters.

Suggested Citation

  • Qingju Fan & Dan Li & Guang Ling & Fang Wang & Shuanggui Liu, 2021. "Effect Of Filters On Multivariate Multifractal Detrended Fluctuation Analysis," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 29(03), pages 1-12, May.
    • Handle: RePEc:wsi:fracta:v:29:y:2021:i:03:n:s0218348x2150047x
    DOI: 10.1142/S0218348X2150047X
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    Citations

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    Cited by:

    1. Li, Jianhui & Li, Qiaozhi & Wang, Fang & Liu, Fan, 2022. "Hyperspectral redundancy detection and modeling with local Hurst exponent," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    2. Wang, Fang & Han, Guosheng, 2023. "Coupling correlation adaptive detrended analysis for multiple nonstationary series," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    3. Wang, Fang & Wang, Lin & Chen, Yuming, 2022. "Multi-affine visible height correlation analysis for revealing rich structures of fractal time series," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).

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