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Generalized entropy plane based on permutation entropy and distribution entropy analysis for complex time series

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  • Dai, Yimei
  • He, Jiayi
  • Wu, Yue
  • Chen, Shijian
  • Shang, Pengjian

Abstract

Entropy is an accessible way to work as a measure of the irregularity and the uncertainty between the predicting knowledge and the given time series. Statistical complexity measure (SCM) combining Shannon entropy and the extensive Jensen–Shannon divergence provides important additional information regarding the peculiarities of the underlying probability distribution, not already detected by the entropy. In this paper, we extend the traditional complexity-entropy causality plane, which applies the diagram of SCM versus normalized Shannon entropy, to two generalized complexity-entropy plane based on Permutation entropy (PE) and Permuted distribution entropy (PEDisEn). Moreover, as the important extension of the Shannon entropy, the Tsallis entropy and Rényi entropy are used to construct the plane. We discuss the parameter selection for the PE plane and PEDisEn plane respectively. Outlier detection is recently a heated point focusing on discovering patterns that occur infrequently in the time series in data mining. However, there exists few entropy plane based methods in outlier detection. We apply the proposed procedure to the real world data for outlier detection. It turns out that the generalized entropy plane is robust to the type of original series and is efficient for detecting outliers.

Suggested Citation

  • Dai, Yimei & He, Jiayi & Wu, Yue & Chen, Shijian & Shang, Pengjian, 2019. "Generalized entropy plane based on permutation entropy and distribution entropy analysis for complex time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 217-231.
  • Handle: RePEc:eee:phsmap:v:520:y:2019:i:c:p:217-231
    DOI: 10.1016/j.physa.2019.01.017
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    References listed on IDEAS

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    1. Douglas M. Hawkins, 1980. "Critical Values for Identifying Outliers," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 95-96, March.
    2. Yang, Pengbo & Shang, Pengjian & Lin, Aijing, 2017. "Financial time series analysis based on effective phase transfer entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 398-408.
    3. Struzik, Zbigniew R. & Siebes, Arno P.J.M., 2002. "Wavelet transform based multifractal formalism in outlier detection and localisation for financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 309(3), pages 388-402.
    4. Zunino, Luciano & Zanin, Massimiliano & Tabak, Benjamin M. & Pérez, Darío G. & Rosso, Osvaldo A., 2010. "Complexity-entropy causality plane: A useful approach to quantify the stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(9), pages 1891-1901.
    5. Wang, Haifeng & Shang, Pengjian & Xia, Jianan, 2016. "Compositional segmentation and complexity measurement in stock indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 67-73.
    6. Tang, Yi & Zhao, An & Ren, Ying-yu & Dou, Fu-Xiang & Jin, Ning-De, 2016. "Gas–liquid two-phase flow structure in the multi-scale weighted complexity entropy causality plane," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 324-335.
    7. Borges, Ernesto P., 2004. "A possible deformed algebra and calculus inspired in nonextensive thermostatistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 340(1), pages 95-101.
    8. Lamberti, P.W & Martin, M.T & Plastino, A & Rosso, O.A, 2004. "Intensive entropic non-triviality measure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 334(1), pages 119-131.
    9. Ribeiro, Haroldo V. & Zunino, Luciano & Mendes, Renio S. & Lenzi, Ervin K., 2012. "Complexity–entropy causality plane: A useful approach for distinguishing songs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2421-2428.
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    Cited by:

    1. Wang, Gangjin & Wei, Daijun & Li, Xiangbo & Wang, Ningkui, 2023. "A novel method for local anomaly detection of time series based on multi entropy fusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    2. Wang, Zhuo & Shang, Pengjian, 2021. "Generalized entropy plane based on multiscale weighted multivariate dispersion entropy for financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    3. Qin, Guyue & Shang, Pengjian, 2021. "Analysis of time series using a new entropy plane based on past entropy," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).

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