Adaptive Clustering and Feature Selection for Categorical Time Series Using Interpretable Frequency-Domain Features.

Abstract

This article presents a novel approach to clustering and feature selection for categorical time series via interpretable frequency-domain features. A distance measure is introduced based on the spectral envelope and optimal scalings, which parsimoniously characterize prominent cyclical patterns in categorical time series. Using this distance, partitional clustering algorithms are introduced for accurately clustering categorical time series. These adaptive procedures offer simultaneous feature selection for identifying important features that distinguish clusters and fuzzy membership when time series exhibit similarities to multiple clusters. Clustering consistency of the proposed methods is investigated, and simulation studies are used to demonstrate clustering accuracy with various underlying group structures. The proposed methods are used to cluster sleep stage time series for sleep disorder patients in order to identify particular oscillatory patterns associated with sleep disruption.