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Modified multiscale entropy for short-term time series analysis

Author

Listed:
  • Wu, Shuen-De
  • Wu, Chiu-Wen
  • Lee, Kung-Yen
  • Lin, Shiou-Gwo

Abstract

Multiscale entropy (MSE) is a prevalent algorithm used to measure the complexity of a time series. Because the coarse-graining procedure reduces the length of a time series, the conventional MSE algorithm applied to a short-term time series may yield an imprecise estimation of entropy or induce undefined entropy. To overcome this obstacle, the modified multiscale entropy (MMSE) was developed. The coarse-graining procedure was replaced with a moving-average procedure and a time delay was incorporated for constructing template vectors in calculating sample entropy. For conducting short-term time series analysis, this study shows that the MMSE algorithm is more reliable than the conventional MSE.

Suggested Citation

  • Wu, Shuen-De & Wu, Chiu-Wen & Lee, Kung-Yen & Lin, Shiou-Gwo, 2013. "Modified multiscale entropy for short-term time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5865-5873.
  • Handle: RePEc:eee:phsmap:v:392:y:2013:i:23:p:5865-5873
    DOI: 10.1016/j.physa.2013.07.075
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    References listed on IDEAS

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    1. Govindan, R.B. & Wilson, J.D. & Eswaran, H. & Lowery, C.L. & Preißl, H., 2007. "Revisiting sample entropy analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 158-164.
    2. Costa, M. & Peng, C.-K. & L. Goldberger, Ary & Hausdorff, Jeffrey M., 2003. "Multiscale entropy analysis of human gait dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 330(1), pages 53-60.
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    Cited by:

    1. Deka, Bhabesh & Deka, Dipen, 2022. "An improved multiscale distribution entropy for analyzing complexity of real-world signals," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    2. Alves Xavier, Sílvio Fernando & Xavier, Érika Fialho Morais & Jale, Jader Silva & Stosic, Tatijana & Santos, Carlos Antonio Costa dos, 2021. "Multiscale entropy analysis of monthly rainfall time series in Paraíba, Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    3. Fan, Xinghua & Li, Shasha & Tian, Lixin, 2016. "Complexity of carbon market from multi-scale entropy analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 79-85.
    4. Palit, Sanjay K. & Mukherjee, Sayan, 2021. "A study on dynamics and multiscale complexity of a neuro system," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    5. Azami, Hamed & Escudero, Javier, 2017. "Refined composite multivariate generalized multiscale fuzzy entropy: A tool for complexity analysis of multichannel signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 261-276.
    6. 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.
    7. Wu, Shuen-De & Wu, Chiu-Wen & Humeau-Heurtier, Anne, 2016. "Refined scale-dependent permutation entropy to analyze systems complexity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 454-461.

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