Local Subsequence-Based Distribution for Time Series Clustering

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.