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Dynamic time warping (DTW) plays an important role in analytics on timeseries. Despite the large body of research on speeding up univariate DTW, themethod for multivariate DTW has not been improved much in the last two decades.The most popular algorithm used today is still the one developed seventeenyears ago. This paper presents a solution that, as far as we know, for thefirst time consistently outperforms the classic multivariate DTW algorithmacross dataset sizes, series lengths, data dimensions, temporal window sizes,and machines. The new solution, named TC-DTW, introduces Triangle Inequalityand Point Clustering into the algorithm design on lower bound calculations formultivariate DTW. In experiments on DTW-based nearest neighbor finding, the newsolution avoids as much as 98% (60% average) DTW distance calculations andyields as much as 25X (7.5X average) speedups.

TC-DTW: Accelerating Multivariate Dynamic Time Warping Through Triangle Inequality and Point Clustering