Time series analysis using persistent homology of distance matrix

Abstract

The analysis of nonlinear dynamics is an important issue in numerous fields of science. In this study, we propose a new method to analyze the time series data using persistent homology (PH). The key idea is the application of PH to the distance matrix. Using this method, we can obtain the topological features embedded in the trajectories. We apply this method to the logistic map, R\"ossler system, and electrocardiogram data. The results reveal that our method can effectively identify nonlocal characteristics of the attractor and can classify data based on the amount of noise.