Enhancing Nearest Neighbor Classification Performance through Dynamic Time Warping with Progressive Constraint

Abstract

Dynamic time warping (DTW) is a widely used metric for comparing time series data, offering elasticity in alignment. While the original DTW allows infinite elasticity without penalty, the wDTW imposes a constant penalty regardless of elastic length. In this study, we propose DTW with a progressive penalty. Experimental evaluations across diverse time series datasets demonstrate the effectiveness of this approach, utilizing nearest neighbor classification. Optimal hyperparameters, including the number of neighbors and progressive weight factor, are jointly identified with the Minkowski p value using Gaussian Process. The proposed methodology shows promise for enhancing performance across various applications leveraging DTW.