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Phase permutation entropy: A complexity measure for nonlinear time series incorporating phase information

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  • Kang, Huan
  • Zhang, Xiaofeng
  • Zhang, Guangbin

Abstract

Based on permutation entropy (PE), which has been presented as a measure to characterize the complexity of nonlinear time series, phase permutation entropy (PPE) is proposed in this paper. Experiments are implemented using artificial and actual data to show the performance of PPE algorithm. The achieved results demonstrate that PPE can amplify the detection effect of dynamical changes compared with PE whether using the logistic map or actual signals. Increasing embedding dimension can improve the capability of detecting dynamical changes using PPE method. Furthermore, PPE is not sensitive to data length when embedding dimension is less than or equal to 5 and it is more susceptible to noise than PE. The results from actual signals show that PPE can be used as an effective analytical tool in the field of biomedical and engineering signals processing.

Suggested Citation

  • Kang, Huan & Zhang, Xiaofeng & Zhang, Guangbin, 2021. "Phase permutation entropy: A complexity measure for nonlinear time series incorporating phase information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 568(C).
    • Handle: RePEc:eee:phsmap:v:568:y:2021:i:c:s0378437120309845
    DOI: 10.1016/j.physa.2020.125686
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    References listed on IDEAS

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    1. Luís Francisco Aguiar & Maria Joana Soares, 2010. "The Continuous Wavelet Transform: A Primer," NIPE Working Papers 23/2010, NIPE - Universidade do Minho.
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    Cited by:

    1. Wan, Li & Ling, Guang & Guan, Zhi-Hong & Fan, Qingju & Tong, Yu-Han, 2022. "Fractional multiscale phase permutation entropy for quantifying the complexity of nonlinear time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    2. Wei, Nan & Yin, Lihua & Li, Chao & Liu, Jinyuan & Li, Changjun & Huang, Yuanyuan & Zeng, Fanhua, 2022. "Data complexity of daily natural gas consumption: Measurement and impact on forecasting performance," Energy, Elsevier, vol. 238(PC).
    3. Lin, Guancen & Lin, Aijing, 2022. "Modified multiscale sample entropy and cross-sample entropy based on horizontal visibility graph," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    4. Lin, Guancen & Lin, Aijing & Mi, Yujia & Gu, Danlei, 2023. "Measurement of information transfer based on phase increment transfer entropy," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).

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