Matrix Profile XXII: Exact Discovery of Time Series Motifs under DTW
This enables motif discovery with DTW for downstream tasks like clustering and classification, addressing a bottleneck in time series analysis.
The paper tackles the problem of discovering time series motifs under Dynamic Time Warping (DTW), which had been computationally challenging, and presents the first scalable exact method that can prune up to 99.99% of DTW computations under realistic settings.
Over the last decade, time series motif discovery has emerged as a useful primitive for many downstream analytical tasks, including clustering, classification, rule discovery, segmentation, and summarization. In parallel, there has been an increased understanding that Dynamic Time Warping (DTW) is the best time series similarity measure in a host of settings. Surprisingly however, there has been virtually no work on using DTW to discover motifs. The most obvious explanation of this is the fact that both motif discovery and the use of DTW can be computationally challenging, and the current best mechanisms to address their lethargy are mutually incompatible. In this work, we present the first scalable exact method to discover time series motifs under DTW. Our method automatically performs the best trade-off between time-to-compute and tightness-of-lower-bounds for a novel hierarchy of lower bounds representation we introduce. We show that under realistic settings, our algorithm can admissibly prune up to 99.99% of the DTW computations.