Dynamic Time Warp (DTW) as a scalable, data-efficient, and clinically relevant analysis of dynamic processes in patients with psychiatric disorders: a tutorial.

Abstract

Dynamic Time Warping (DTW) is an emerging analytic technique that offers a flexible approach to modeling symptom dynamics in psychological and psychiatric research. Unlike traditional network models, which often rely on linear associations, DTW aligns symptom trajectories even when changes unfold at slightly different speeds or time intervals. This tutorial offers a brief introduction into DTW and demonstrates how to apply DTW to panel or time series data. We illustrate the workflow using clinical case data from patients with eating disorders, to capture temporal patterns that cannot be detected with conventional network analysis techniques, as these require more intensive time-series data. Key advantages include its applicability to non-stationary data, flexibility in handling irregular time intervals, and reduced reliance on frequent assessments, which patients often cannot maintain due to the burden. We also discuss some of the limitations such as noise, scaling decisions and lack of Granger causality associations. Finally, we outline directions for future research. By expanding the methodological toolkit available for studying therapy processes, DTW holds promise for advancing both research and clinical practice in personalized mental health care.