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Detrended Cross-Correlation Analysis: A New Method for Analyzing Two Non-stationary Time Series

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  • Boris Podobnik
  • H. Eugene Stanley

Abstract

Here we propose a method, based on detrended covariance which we call detrended cross-correlation analysis (DXA), to investigate power-law cross-correlations between different simultaneously-recorded time series in the presence of non-stationarity. We illustrate the method by selected examples from physics, physiology, and finance.

Suggested Citation

  • Boris Podobnik & H. Eugene Stanley, 2007. "Detrended Cross-Correlation Analysis: A New Method for Analyzing Two Non-stationary Time Series," Papers 0709.0281, arXiv.org.
    • Handle: RePEc:arx:papers:0709.0281
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    Cited by:

    1. Nurbanu Bursa & Hüseyin Tatlýdil, 2015. "Investigation of Credit Default Swaps using Detrended Fluctuation Analysis which is an Econophysical Technique," Eurasian Eononometrics, Statistics and Emprical Economics Journal, Eurasian Academy Of Sciences, vol. 2(2), pages 25-33, October.
    2. Zhang, Tonghui & Yuan, Ying & Wu, Xi, 2020. "Is microblogging data reflected in stock market volatility? Evidence from Sina Weibo," Finance Research Letters, Elsevier, vol. 32(C).
    3. Yao, Can-Zhong & Lin, Ji-Nan & Zheng, Xu-Zhou, 2017. "Coupling detrended fluctuation analysis for multiple warehouse-out behavioral sequences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 75-90.
    4. Teng, Yue & Shang, Pengjian, 2017. "Transfer entropy coefficient: Quantifying level of information flow between financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 60-70.
    5. Dai, Yimei & Zhang, Hesheng & Mao, Xuegeng & Shang, Pengjian, 2018. "Complexity–entropy causality plane based on power spectral entropy for complex time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 501-514.

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