An adaptive time series segmentation algorithm based on visibility graph and particle swarm optimization

Abstract

Time series segmentation is a crucial area of research in time series analysis as it can reveal meaningful patterns or segments hidden within time series data. In this paper, we present an accurate and efficient time series segmentation method that combines the visibility graph method, particle swarm optimization, and community detection algorithm. We start by applying visibility graph theory to process time series data, resulting in a corresponding complex network. Next, we introduce an adaptive particle swarm optimization algorithm with modularity Q as the objective function to optimize community detection. Finally, mapping the communities back to the nodes of the time series yields the segmented sequence. Our proposed method offers high segmentation accuracy and low time complexity (O(n2)). Experimental results demonstrate that our approach outperforms existing methods in terms of segmentation accuracy on two different synthetic datasets. Furthermore, when applied to the S&P500 index dataset, it accurately identifies financial cycles and key financial events.