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Multivariate multiscale complexity-entropy causality plane analysis for complex time series

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Abstract

The multivariate multiscale complexity-entropy causality plane (MMCECP) is introduced for evaluating the dynamical complexity and long-range correlations of multivariate nonlinear systems. Numerical simulations from different classes of systems are applied to confirm the effectiveness of the proposed measure. We observe that the MMCECP not only can characterize the deterministic properties of the systems, but also can distinguish Gaussian and non-Gaussian processes. Moreover, it is immune to varying degrees of noises at large scales. Then we apply it to financial time series analysis, mainly investigating the classification of stock market dynamics. Empirical results illustrate that the MMCECP is robust and valid to detect the physical structures of stock markets.

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Acknowledgements

The financial supports from the funds of the Fundamental Research Funds for the Central Universities (2019YJS205, 2018JBZ104), the China National Science (61771035) and the Beijing National Science (4162047) are gratefully acknowledged.

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Authors and Affiliations

  1. Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing, 100044, People’s Republic of China

    Xuegeng Mao & Pengjian Shang

  2. Lab for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, People’s Republic of China

    Qinglei Li

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  1. Xuegeng Mao
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Mao, X., Shang, P. & Li, Q. Multivariate multiscale complexity-entropy causality plane analysis for complex time series. Nonlinear Dyn 96, 2449–2462 (2019). https://doi.org/10.1007/s11071-019-04933-7

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