Analysis of complex time series based on EEMD energy entropy plane

Shujia Wang, Zhuojin Yang, Xin Zheng and Zhuang Ma

Chaos, Solitons & Fractals, 2024, vol. 182, issue C

Abstract: Ensemble empirical mode decomposition (EEMD) is an adaptive signal processing method that can be effectively applied to nonlinear and non-stationary complex systems. Due to this good attribute, this paper proposes the EEMD energy entropy plane, which combines the EEMD energy entropy and the complexity-entropy causality plane to measure the essential characteristics of the time series. First, we apply the EEMD energy entropy plane to the Logistics map, Hénon map and Lozi map. We find that the EEMD energy entropy can accurately capture the periodic state and chaotic state of the system and carry them out classification. Then we apply the EEMD energy entropy plane to the stock market. The results show that this method can distinguish the stock market in different geographical areas such as economic policy, legal environment, cultural and social environment, industrial structure, etc., and classify them correctly. In the validity test, EEMD energy entropy shows robustness as the signal-to-noise ratio increases and the sequence length changes.

Keywords: Ensemble empirical mode decomposition; Complexity-entropy causality plane; Energy entropy; Financial time series (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:182:y:2024:i:c:s0960077924004181

DOI: 10.1016/j.chaos.2024.114866

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