TC-DTW: Accelerating Multivariate Dynamic Time Warping Through Triangle Inequality and Point Clustering
This provides a significant speedup for time series analytics, benefiting researchers and practitioners in fields like data mining and machine learning, though it is an incremental improvement over existing multivariate DTW methods.
The paper tackles the problem of accelerating multivariate dynamic time warping (DTW), which has seen little improvement in two decades, by introducing TC-DTW, a method that uses triangle inequality and point clustering to reduce distance calculations. It achieves up to 98% reduction in calculations and 25X speedups in experiments.
Dynamic time warping (DTW) plays an important role in analytics on time series. Despite the large body of research on speeding up univariate DTW, the method for multivariate DTW has not been improved much in the last two decades. The most popular algorithm used today is still the one developed seventeen years ago. This paper presents a solution that, as far as we know, for the first time consistently outperforms the classic multivariate DTW algorithm across dataset sizes, series lengths, data dimensions, temporal window sizes, and machines. The new solution, named TC-DTW, introduces Triangle Inequality and Point Clustering into the algorithm design on lower bound calculations for multivariate DTW. In experiments on DTW-based nearest neighbor finding, the new solution avoids as much as 98% (60% average) DTW distance calculations and yields as much as 25X (7.5X average) speedups.