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Symbolic convergent cross mapping based on permutation mutual information

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  • Ge, Xinlei
  • Lin, Aijing

Abstract

In this paper, we extend convergent cross mapping (CCM) and propose symbolic CCM (SCCM), which uses mutual information based on permutation pattern instead of Pearson correlation coefficient to estimate cross-mapping ability. We numerically demonstrate that SCCM is a robust method for quantifying information flow between time series in chaotic systems, even under the influence of noises. Using the method, we analyze the multichannel EEG signals of ADHD children and control children, and identify the differences between the two groups of subjects with reliable results.

Suggested Citation

  • Ge, Xinlei & Lin, Aijing, 2023. "Symbolic convergent cross mapping based on permutation mutual information," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    • Handle: RePEc:eee:chsofr:v:167:y:2023:i:c:s0960077922011717
    DOI: 10.1016/j.chaos.2022.112992
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    References listed on IDEAS

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    1. Albert C. Yang & Chung-Kang Peng & Norden E. Huang, 2018. "Causal decomposition in the mutual causation system," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    2. Jakob Runge & Sebastian Bathiany & Erik Bollt & Gustau Camps-Valls & Dim Coumou & Ethan Deyle & Clark Glymour & Marlene Kretschmer & Miguel D. Mahecha & Jordi Muñoz-Marí & Egbert H. Nes & Jonas Peters, 2019. "Inferring causation from time series in Earth system sciences," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
    3. Guo, Zhen & Hao, Mengyan & Yu, Bin & Yao, Baozhen, 2022. "Detecting delay propagation in regional air transport systems using convergent cross mapping and complex network theory," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
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