Analysis of nonlinear time series using discrete generalized past entropy based on amplitude difference distribution of horizontal visibility graph

In this paper, we propose discrete generalized past entropy based on amplitude difference distribution of horizontal visibility graph as a new complexity measure of nonlinear time series. We use amplitude difference distribution instead of degree distribution to extract information from the network constructed from the horizontal visibility graph, and combine amplitude difference distribution with discrete generalized past entropy to propose the new method. By analyzing the logistic map and H & eacute;non map with the proposed method, we find the proposed method not only can assess systems well, but also has higher accuracy and sensitivity than the traditional method in characterizing dynamical systems. Furthermore, we apply the proposed method to the financial data: the six indices from Chinese mainland, Hong Kong and US. The result shows that the US market and the Hong Kong market are more developed than the Chinese mainland market, which is consistent with the reality. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The paper proposes a new complexity measure for nonlinear time series, utilizing amplitude difference distribution and discrete generalized past entropy. By applying the method to analyze logistic and Henon maps, it is demonstrated to have higher accuracy and sensitivity in assessing dynamical systems compared to traditional methods. Additionally, the method is successfully applied to financial data, indicating the development levels of US and Hong Kong markets relative to the Chinese mainland market.