Dynamic Time Warping: Itakura vs Sakoe-Chiba

Abstract

© 2019 IEEE. In the domain of time-series classification, one simple but persistently successful method is the 1-nearest neighbour (1NN) classifier coupled with an elastic distance measure such as Dynamic Time Warping (DTW). In this paper we evaluate the performance of DTW when constrained using the Itakura parallelogram, and compare it with the more commonly used Sakoe-Chiba band, as well as with the unconstrained DTW. Results show that although the Itakura parallelogram is generally inferior to the Sakoe-Chiba band, it is still superior to unconstrained DTW. Furthermore, on individual data sets the Itakura parallelogram can produce superior results, warranting further investigation into the merits of its use with DTW and other elastic distance measures for time-series classification.