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Chaotic SVD method for minimizing the effect of exponential trends in detrended fluctuation analysis

Author

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  • Shang, Pengjian
  • Lin, Aijing
  • Liu, Liang

Abstract

The Detrended Fluctuation Analysis (DFA) and its extensions (MF-DFA) have been used extensively to determine possible long-range correlations in self-affine signals. However, recent studies have reported the susceptibility of DFA to trends which give rise to spurious crossovers and prevent reliable estimation of the scaling exponents. In this study, a smoothing algorithm based on the Chaotic Singular-Value Decomposition (CSVD) is proposed to minimize the effect of exponential trends and distortion in the log–log plots obtained by DFA techniques. The effectiveness of the technique is demonstrated on monofractal and multifractal data corrupted with exponential trends.

Suggested Citation

  • Shang, Pengjian & Lin, Aijing & Liu, Liang, 2009. "Chaotic SVD method for minimizing the effect of exponential trends in detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(5), pages 720-726.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:5:p:720-726
    DOI: 10.1016/j.physa.2008.10.044
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    Citations

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    Cited by:

    1. Chi Zhang & Zhengning Pu & Qin Zhou, 2018. "Sustainable Energy Consumption in Northeast Asia: A Case from China’s Fuel Oil Futures Market," Sustainability, MDPI, vol. 10(1), pages 1-14, January.
    2. Lucheng Hong & Wantao Shu & Angela C. Chao, 2018. "Recurrence Interval Analysis on Electricity Consumption of an Office Building in China," Sustainability, MDPI, vol. 10(2), pages 1-15, January.
    3. Zhao, Xiaojun & Shang, Pengjian & Lin, Aijing & Chen, Gang, 2011. "Multifractal Fourier detrended cross-correlation analysis of traffic signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3670-3678.
    4. Jiang, Zhi-Qiang & Xie, Wen-Jie & Zhou, Wei-Xing, 2014. "Testing the weak-form efficiency of the WTI crude oil futures market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 235-244.
    5. Yin, Yi & Shang, Pengjian & Ahn, Andrew C. & Peng, Chung-Kang, 2019. "Multiscale joint permutation entropy for complex time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 388-402.
    6. Xie, Wen-Jie & Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2014. "Extreme value statistics and recurrence intervals of NYMEX energy futures volatility," Economic Modelling, Elsevier, vol. 36(C), pages 8-17.
    7. Jiang, Chenguang & Shang, Pengjian & Shi, Wenbin, 2016. "Multiscale multifractal time irreversibility analysis of stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 492-507.
    8. Li, Hongtao & Gedikli, Ersegun Deniz & Lubbad, Raed, 2020. "Exploring time-delay-based numerical differentiation using principal component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    9. Yang, Yujun & Li, Jianping & Yang, Yimei, 2017. "The cross-correlation analysis of multi property of stock markets based on MM-DFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 23-33.
    10. Zhao, Xiaojun & Shang, Pengjian & Zhao, Chuang & Wang, Jing & Tao, Rui, 2012. "Minimizing the trend effect on detrended cross-correlation analysis with empirical mode decomposition," Chaos, Solitons & Fractals, Elsevier, vol. 45(2), pages 166-173.

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