Time series analysis using composite multiscale entropy
type: Item
instance of: scholarly article
title: Time series analysis using composite multiscale entropy
zbMATH Open document ID: 1298.65020
author: Shuen-De Wu
author: Chiu-Wen Wu
author: Shiou-Gwo Lin
author: Chun-Chieh Wang
author: Kung-Yen Lee
published in: Entropy
publication date: 2014-09-19T00:00:00Z
review text: Summary: Multiscale entropy (MSE) was recently developed to evaluate the complexity of time series over different time scales. Although the MSE algorithm has been successfully applied in a number of different fields, it encounters a problem in that the statistical reliability of the sample entropy (SampEn) of a coarse-grained series is reduced as a time scale factor is increased. Therefore, in this paper, the concept of a composite multiscale entropy (CMSE) is introduced to overcome this difficulty. Simulation results on both white noise and \(1/f\) noise show that the CMSE provides higher entropy reliablity than the MSE approach for large time scale factors. On real data analysis, both the MSE and CMSE are applied to extract features from fault bearing vibration signals. Experimental results demonstrate that the proposed CMSE-based feature extractor provides higher separability than the MSE-based feature extractor.
Mathematics Subject Classification ID: 65C60
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 94A17
Mathematics Subject Classification ID: 62-07
zbMATH DE Number: 6346100
zbMATH Keywords: composite multiscale entropy
zbMATH Keywords: multiscale entropy
zbMATH Keywords: fault diagnosis
zbMATH Keywords: numerical examples
zbMATH Keywords: time series
zbMATH Keywords: algorithm
zbMATH Keywords: statistical reliability
zbMATH Keywords: data analysis
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/e15031069
OpenAlex ID: W2041287404
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DOI: 10.3390/E15031069
description: scientific article; zbMATH DE number 6346100