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Analysis of complex time series based on EMD energy entropy plane

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Abstract

Empirical mode decomposition (EMD) is a self-adaptive signal processing method that can be applied to nonlinear and non-stationary processes perfectly. In view of this good ability of EMD, in this paper, we propose a new method—EMD energy entropy plane—which combines two different tools—EMD energy entropy and complexity-entropy causality plane—to analyze time series. Firstly, we apply EMD energy entropy plane to synthetic data, such as logistic map, Hénon map, ARFIMA model and so on, finding that the EMD energy entropy plane presents different trends and distributions when the map is in periodic cycles and chaos. Then we demonstrate the application of EMD energy entropy plane in stock markets. Results show that it is an effective tool of distinguishing two kinds of financial markets. In addition, the introduction of multi-scale reveals the variation law of EMD energy entropy plane at different scales.

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Acknowledgements

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

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

    Jing Gao & Pengjian Shang

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  1. Jing Gao
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  2. Pengjian Shang
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Correspondence to Jing Gao.

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Gao, J., Shang, P. Analysis of complex time series based on EMD energy entropy plane. Nonlinear Dyn 96, 465–482 (2019). https://doi.org/10.1007/s11071-019-04800-5

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