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EMD based refined composite multiscale entropy analysis of complex signals

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  • Wang, Jing
  • Shang, Pengjian
  • Xia, Jianan
  • Shi, Wenbin

Abstract

Multiscale entropy (MSE) is an effective method to measure the complexity of signals from complex systems, which has been applied to various fields successfully. However, MSE may yield an inaccurate estimate of entropy and induce undefined entropy as the coarse-graining procedure reduces the length of data considerably at large scales. Refined composite multiscale entropy (RCMSE) is then developed to solve this problem. However, trends superimposed in signals may significantly affect the complexity measurement. Thus we introduce an empirical mode decomposition based RCMSE, called EMD–RCMSE to first eliminate such effects of trends and then measure the complexity of signals. It is validated from simulated signals and has good estimation. In addition, this method is also applied to study the complexity of traffic signals and we obtain some interesting results: (1) Traffic signals are more complex than the results showing from RCMSE/MSE; (2) Weekday and weekend patterns (different combination of trends) greatly affect the results; (3) Complexity indices change with time at each day, due to the degree of human activities.

Suggested Citation

  • Wang, Jing & Shang, Pengjian & Xia, Jianan & Shi, Wenbin, 2015. "EMD based refined composite multiscale entropy analysis of complex signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 583-593.
    • Handle: RePEc:eee:phsmap:v:421:y:2015:i:c:p:583-593
    DOI: 10.1016/j.physa.2014.12.001
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    References listed on IDEAS

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    1. Ansorge, Rainer, 1990. "What does the entropy condition mean in traffic flow theory?," Transportation Research Part B: Methodological, Elsevier, vol. 24(2), pages 133-143, April.
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    3. Xia, Jianan & Shang, Pengjian & Wang, Jing & Shi, Wenbin, 2014. "Classifying of financial time series based on multiscale entropy and multiscale time irreversibility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 151-158.
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    Cited by:

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    3. Wang, Shujia & Yang, Zhuojin & Zheng, Xin & Ma, Zhuang, 2024. "Analysis of complex time series based on EEMD energy entropy plane," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
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    6. Xiong, Hui & Shang, Pengjian & Bian, Songhan, 2017. "Detecting intrinsic dynamics of traffic flow with recurrence analysis and empirical mode decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 70-84.
    7. Brechtl, Jamieson & Xie, Xie & Liaw, Peter K. & Zinkle, Steven J., 2018. "Complexity modeling and analysis of chaos and other fluctuating phenomena," Chaos, Solitons & Fractals, Elsevier, vol. 116(C), pages 166-175.
    8. Liu, Yunxiao & Lin, Youfang & Wang, Jing & Shang, Pengjian, 2018. "Refined generalized multiscale entropy analysis for physiological signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 975-985.
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    10. Xia, Jianan & Shang, Pengjian & Lu, Dan & Yin, Yi, 2016. "A comprehensive segmentation analysis of crude oil market based on time irreversibility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 104-114.

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