Zhang Zelin, Wu Jun, Chen Yufeng, Wang Ji, Xu Jinyu
School of Mathematics, Physics and Optoelectronic Engineering, Hubei University of Automotive Technology, Shiyan 442002, China.
Hubei Key Laboratory of Applied Mathematics, Hubei University, Wuhan 430061, China.
Entropy (Basel). 2022 Nov 30;24(12):1752. doi: 10.3390/e24121752.
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 LSEFSTE100 and LSES&P500 are higher than LSESZI, 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.
作为一种复杂性度量，信息熵经常被用于对时间序列进行分类，如机械故障诊断、生物信号识别等，并被视为动态系统的一个特征。然而，由于相关性，许多熵在多变量场景中无效。在本文中，我们基于递归网络的思想提出了一种局部结构熵（LSE）。给定一定的容差和尺度，LSE值可以区分随机信号中的多变量混沌序列。使用三个金融市场指数来评估所提出的LSE。结果表明，LSEFSTE100和LSES&P500高于LSESZI，这表明欧美股票市场比中国股票市场更复杂。此外，使用决策树作为分类器，LSE被用于检测轴承故障。与排列熵相比，LSE在识别准确率上表现更高。
