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The dependence measurements based on martingale difference correlation and distance correlation: Efficient tools to distinguish different complex systems

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  • Shang, Du
  • Shang, Pengjian

Abstract

The study of the relationships between time series is the basis of nonlinear dynamics and especially plays an important role in revealing the dynamical relationships between different systems. So in this work, we use the volatility martingale difference divergence matrix (VMDDM) to construct the dependence index (DI) from the perspective of the martingale difference correlation (MDC) and the phase space reconstruction. The dependence measure (DM) is also structured based on the phase space reconstruction and the refined version of distance correlation (RDC), which involves a modified distance dependence statistic. Through simulation experiments, it is demonstrated that our proposed methods are efficient in discovering various subtle dynamical characteristics. For applications, data from the real world are used, containing human heartbeat data, stocks indices series, data of complex image outlines, artificial CBF data, and data of food (coffee) spectrum. We are sure that the proposed approaches can obtain information with more specifics when compared with the state-of-the-art cross-sample entropy (CSE) and the frequently used synchrony measuring technique, the synchronization index (SI). Moreover, for complex systems, we construct the DM-tr(VMDDM) plane to achieve distinct and reasonable results of clustering.

Suggested Citation

  • Shang, Du & Shang, Pengjian, 2022. "The dependence measurements based on martingale difference correlation and distance correlation: Efficient tools to distinguish different complex systems," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    • Handle: RePEc:eee:chsofr:v:156:y:2022:i:c:s0960077922000790
    DOI: 10.1016/j.chaos.2022.111868
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    References listed on IDEAS

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    1. Zhou Zhou, 2012. "Measuring nonlinear dependence in time‐series, a distance correlation approach," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(3), pages 438-457, May.
    2. Székely, Gábor J. & Rizzo, Maria L., 2013. "The distance correlation t-test of independence in high dimension," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 193-213.
    3. Székely, Gábor J. & Rizzo, Maria L., 2012. "On the uniqueness of distance covariance," Statistics & Probability Letters, Elsevier, vol. 82(12), pages 2278-2282.
    4. Xiaofeng Shao & Jingsi Zhang, 2014. "Martingale Difference Correlation and Its Use in High-Dimensional Variable Screening," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1302-1318, September.
    5. Chung Eun Lee & Xiaofeng Shao, 2018. "Martingale Difference Divergence Matrix and Its Application to Dimension Reduction for Stationary Multivariate Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 216-229, January.
    6. Afshin Jamshidi & Daoud Ait-kadi & Angel Ruiz & Mohamed Larbi Rebaiaia, 2018. "Dynamic risk assessment of complex systems using FCM," International Journal of Production Research, Taylor & Francis Journals, vol. 56(3), pages 1070-1088, February.
    7. Chen, Shijian & Shang, Pengjian & Wu, Yue, 2019. "Multivariate multiscale fractional order weighted permutation entropy of nonlinear time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 217-231.
    8. Lamberti, P.W & Martin, M.T & Plastino, A & Rosso, O.A, 2004. "Intensive entropic non-triviality measure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 334(1), pages 119-131.
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