Shape-based template matching for time series data

Abstract

Dynamic time warping (DTW) distance has been proven to be one of the most accurate distance measures for time series classification. However, its calculation complexity is its own major drawback, especially when a massive training database has to be searched. Although many techniques have been proposed to speed up the search including indexing structures and lower bounding functions, for large databases, it is still untenable to embed the algorithm and search through the entire database of a system with limited resources, e.g., tiny sensors, within a given time. Therefore, a template matching is a solution to efficiently reduce storage and computation requirements; in other words, only a few time series sequences have to be retrieved and compared with an incoming query data. In this work, we propose a novel template matching framework with the use of DTW distance, where a shape-based averaging algorithm is utilized to construct meaningful templates. Our proposed framework demonstrates its utilities, where classification time speedup is in orders of magnitude, while maintaining good accuracy to rival methods.