Local Subsequence-Based Distribution for Time Series Clustering

Lei Gong, Hang Zhang, Zongyou Liu, Kai Ming Ting, Yang Cao, Ye Zhu

Abstract: Analyzing the properties of subsequences within time series can reveal hidden patterns and improve the quality of time series clustering. However, most existing methods for subsequence analysis require point-to-point alignment, which is sensitive to shifts and noise. In this paper, we propose a clustering method named CTDS that treats time series as a set of independent and identically distributed (iid) points in \(\mathbb {R}^d\) extracted by a sliding window in local regions. CTDS utilises a distributional measure called Isolation Distributional Kernel (IDK) that can capture the subtle differences between probability distributions of subsequences without alignment. It has the ability to cluster large non-stationary and complex datasets. We evaluate CTDS on UCR time series benchmark datasets and demonstrate its superior performance than other state-of-the-art clustering methods.