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Generalized sample entropy analysis for traffic signals based on similarity measure

Author

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  • Shang, Du
  • Xu, Mengjia
  • Shang, Pengjian

Abstract

Sample entropy is a prevailing method used to quantify the complexity of a time series. In this paper a modified method of generalized sample entropy and surrogate data analysis is proposed as a new measure to assess the complexity of a complex dynamical system such as traffic signals. The method based on similarity distance presents a different way of signals patterns match showing distinct behaviors of complexity. Simulations are conducted over synthetic data and traffic signals for providing the comparative study, which is provided to show the power of the new method. Compared with previous sample entropy and surrogate data analysis, the new method has two main advantages. The first one is that it overcomes the limitation about the relationship between the dimension parameter and the length of series. The second one is that the modified sample entropy functions can be used to quantitatively distinguish time series from different complex systems by the similar measure.

Suggested Citation

  • Shang, Du & Xu, Mengjia & Shang, Pengjian, 2017. "Generalized sample entropy analysis for traffic signals based on similarity measure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 1-7.
    • Handle: RePEc:eee:phsmap:v:474:y:2017:i:c:p:1-7
    DOI: 10.1016/j.physa.2017.01.061
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    References listed on IDEAS

    as
    1. Shi, Wenbin & Shang, Pengjian & Xia, Jianan & Yeh, Chien-Hung, 2016. "The coupling analysis between stock market indices based on permutation measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 222-231.
    2. Shang, Pengjian & Lu, Yongbo & Kamae, Santi, 2008. "Detecting long-range correlations of traffic time series with multifractal detrended fluctuation analysis," Chaos, Solitons & Fractals, Elsevier, vol. 36(1), pages 82-90.
    3. Schadschneider, Andreas, 2000. "Statistical physics of traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 285(1), pages 101-120.
    4. Xavier Gabaix & Parameswaran Gopikrishnan & Vasiliki Plerou & H. Eugene Stanley, 2003. "A theory of power-law distributions in financial market fluctuations," Nature, Nature, vol. 423(6937), pages 267-270, May.
    5. Shang, Pengjian & Li, Xuewei & Kamae, Santi, 2005. "Chaotic analysis of traffic time series," Chaos, Solitons & Fractals, Elsevier, vol. 25(1), pages 121-128.
    6. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    7. Zunino, L. & Tabak, B.M. & Figliola, A. & Pérez, D.G. & Garavaglia, M. & Rosso, O.A., 2008. "A multifractal approach for stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(26), pages 6558-6566.
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    Cited by:

    1. Pham, Tuan D. & Yan, Hong, 2018. "Spatial-dependence recurrence sample entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 581-590.
    2. He, Shaobo & Banerjee, Santo, 2018. "Multicavity formations and complexity modulation in a hyperchaotic discrete system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 366-377.

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