Modified multiscale entropy for short-term time series analysis

Shuen De Wu, Chiu Wen Wu, Kung Yen Lee, Shiou Gwo Lin

Research output: Contribution to journalArticlepeer-review

93 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)5865-5873
Number of pages9
JournalPhysica A: Statistical Mechanics and its Applications
Volume392
Issue number23
DOIs
Publication statusPublished - 2013 Dec 1

Keywords

  • Multiscale entropy (MSE)
  • Sample entropy
  • Short-term time series

ASJC Scopus subject areas

  • Statistics and Probability
  • Condensed Matter Physics

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