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The complexity–entropy causality plane based on multivariate multiscale distribution entropy of traffic time series

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Abstract

The complexity of time series has become a necessary condition to explain nonlinear dynamic systems. We propose multivariate multiscale distribution entropy (MMSDE). Based on this method, this paper evaluates the complexity of traffic system with complexity-entropy causality plane (CEPE). The distribution entropy makes full use of the distance between vectors in the state space and calculates the probability density information to estimate the complexity of the system. And MMSDE can quantify the complexity of multivariable time series from multiple time scales. We test the performance of this method with simulated data. The results show that CEPE based on MMSDE is less dependent on parameters. The complex entropy plane method proposed here has strong anti-interference ability and strong robustness.

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Acknowledgements

The financial supports from the Fundamental Research Funds for the Central Universities (2017YJS199) and the funds of the China National Science (61771035,61371130) and the Beijing National Science (4162047) are gratefully acknowledged.

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

    Yali Zhang & Pengjian Shang

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  1. Yali Zhang
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  2. Pengjian Shang
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Correspondence to Yali Zhang.

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Zhang, Y., Shang, P. The complexity–entropy causality plane based on multivariate multiscale distribution entropy of traffic time series. Nonlinear Dyn 95, 617–629 (2019). https://doi.org/10.1007/s11071-018-4586-2

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