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Distinguish between Stochastic and Chaotic Signals by a Local Structure-Based Entropy.

Authors :
Zhang, Zelin
Wu, Jun
Chen, Yufeng
Wang, Ji
Xu, Jinyu
Source :
Entropy; Dec2022, Vol. 24 Issue 12, p1752, 20p
Publication Year :
2022

Abstract

As a measure of complexity, information entropy is frequently used to categorize time series, such as machinery failure diagnostics, biological signal identification, etc., and is thought of as a characteristic of dynamic systems. Many entropies, however, are ineffective for multivariate scenarios due to correlations. In this paper, we propose a local structure entropy (LSE) based on the idea of a recurrence network. Given certain tolerance and scales, LSE values can distinguish multivariate chaotic sequences between stochastic signals. Three financial market indices are used to evaluate the proposed LSE. The results show that the L S E F S T E 100 and L S E S & P 500 are higher than L S E S Z I , which indicates that the European and American stock markets are more sophisticated than the Chinese stock market. Additionally, using decision trees as the classifiers, LSE is employed to detect bearing faults. LSE performs higher on recognition accuracy when compared to permutation entropy. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10994300
Volume :
24
Issue :
12
Database :
Complementary Index
Journal :
Entropy
Publication Type :
Academic Journal
Accession number :
160984264
Full Text :
https://doi.org/10.3390/e24121752