Locally Slope-based Dynamic Time Warping for Time Series Classification

Abstract

Dynamic time warping (DTW) has been widely used in various domains of daily life. Essentially, DTW is a non-linear point-to-point matching method under time consistency constraints to find the optimal path between two temporal sequences. Although DTW achieves a globally optimal solution, it does not naturally capture locally reasonable alignments. Concretely, two points with entirely dissimilar local shape may be aligned. To solve this problem, we propose a novel weighted DTW based on local slope feature (LSDTW), which enhances DTW by taking regional information into consideration. LSDTW is inherently a DTW algorithm. However, it additionally attempts to pair locally similar shapes, and to avoid matching points with distinct neighborhood slopes. Furthermore, when LSDTW is used as a similarity measure in the popular nearest neighbor classifier, it beats other distance-based methods on the vast majority of public datasets, with significantly improved classification accuracies. In addition, case studies establish the interpretability of the proposed method.