Classification of Categorical Time Series Using the Spectral Envelope and Optimal Scalings

TL;DR

This paper presents a new feature-based classification method for categorical time series using spectral envelope and optimal scalings, demonstrating high accuracy and interpretability in diverse applications including sleep disorder analysis.

Contribution

It introduces a novel spectral envelope and optimal scalings approach for categorical time series classification, enhancing interpretability and accuracy over existing methods.

Findings

High classification accuracy demonstrated in simulations

Effective differentiation of sleep disorder patterns

Method shows consistency in theoretical analysis

Abstract

This article introduces a novel approach to the classification of categorical time series under the supervised learning paradigm. To construct meaningful features for categorical time series classification, we consider two relevant quantities: the spectral envelope and its corresponding set of optimal scalings. These quantities characterize oscillatory patterns in a categorical time series as the largest possible power at each frequency, or spectral envelope, obtained by assigning numerical values, or scalings, to categories that optimally emphasize oscillations at each frequency. Our procedure combines these two quantities to produce an interpretable and parsimonious feature-based classifier that can be used to accurately determine group membership for categorical time series. Classification consistency of the proposed method is investigated, and simulation studies are used to demonstrate accuracy in classifying categorical time series with various underlying group structures. Finally, we use the proposed method to explore key differences in oscillatory patterns of sleep stage time series for patients with different sleep disorders and accurately classify patients accordingly.