A Scalable Segmented Dynamic Time Warping for Time Series Classification

Abstract

The Dynamic Time Warping (DTW) algorithm is an elastic distance measure that has demonstrated good performance with sequence-based data, and in particular, time series data. Two major drawbacks of DTW are the possibility of pathological warping paths and the high computational cost. Improvement techniques such as pruning off impossible mappings or lowering data dimensions have been proposed to counter these issues. The existing DTW improvement techniques, however, are either limited in effect or use accuracy as a trade-off. In this paper, we introduce segmented-DTW (segDTW). A novel and scalable approach that would speed up the DTW algorithm, especially for longer sequences. Our heuristic approaches the time series mapping problem by identifying global similarity before local similarity. This global to local process initiates with easily identified global peaks. Based on these peaks, time series sequences are segmented to sub-sequences, and DTW is applied in a divide-and-compute fashion. By doing so, the computation naturally expands to the parallel case. Due to the paired peaks, our method can avoid some pathological warpings and is highly scalable. We tested our method on a variety of datasets and obtained a gradient of speedup relative to the time series sequence length while maintaining comparable classification accuracy.