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Multiscale Tsallis permutation entropy analysis for complex physiological time series

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  • li, Chao
  • Shang, Pengjian

Abstract

Discussing the complexity of time series has been a long-standing problem, including the use of information entropy to determine the complexity of the sequence. Permutation entropy (PE) has been regarded as a learning process to investigate the complexity of time series, such as financial time series and physiological time series. The permutation entropy, which is based on the Shannon entropy (SE), has undeniable shortcomings in dealing with some specific problems. Hence in this paper, we propose the multiscale Tsallis permutation entropy (MTPE) as an improved measuring tool for assessing the hidden temporal correlations in time series. The modified method not only presents a different way showing clear characteristics but also provides more significant results compared with the SE. Robustness of the hypothesis is proved by the correlation between Tsallis permutation entropy (TPE) and HURST exponent obtained from the sequences which are generated from fractional brownian motion stochastic model. Experimental results of autoregressive sequences (AR) and electroencephalograph time series (EEG) make the advantages of Tsallis permutation entropy more obvious, which also well validate the efficiency and integrity of principle of maximum entropy (PME). Multiscale analysis provides us with another perspective to analyze permutation entropy, which also allows us to better analyze the complexity of the sequence.

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  • li, Chao & Shang, Pengjian, 2019. "Multiscale Tsallis permutation entropy analysis for complex physiological time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 10-20.
    • Handle: RePEc:eee:phsmap:v:523:y:2019:i:c:p:10-20
    DOI: 10.1016/j.physa.2019.01.031
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    References listed on IDEAS

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    1. Jing Wang & Pengjian Shang & Xiaojun Zhao & Jianan Xia, 2013. "Multiscale Entropy Analysis Of Traffic Time Series," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 24(02), pages 1-14.
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    4. Yin, Yi & Shang, Pengjian, 2015. "Modified cross sample entropy and surrogate data analysis method for financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 17-25.
    5. Wenbin Shi & Pengjian Shang, 2015. "The multiscale analysis between stock market time series," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 26(06), pages 1-12.
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