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

Presented by Scott Bruce, Assistant Professor, Department of Statistics, Texas A & M University

Wednesday, September 14
4:00 p.m. ET
Virtual

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.

Speaker Bio:

Dr. Bruce is an assistant professor in the Department of Statistics at Texas A&M University. He completed his doctoral training at Temple University where he developed statistical methodology for frequency-domain analysis of time series. He is passionate about creating novel computationally-efficient statistical methods for the analysis of time series and longitudinal data in areas such as sleep research, neuroscience, and psychiatry. He is involved in numerous transdisciplinary projects in these areas and aims to produce high-quality publications in top statistics and scientific journals. His research interests include nonstationary time series, spectral analysis, Bayesian statistical learning, computational data science, longitudinal data analysis, transdisciplinary research, applications in sleep medicine, biomechanics, neuroscience, and psychiatry.