This website includes all the codes, data, experiments and the figures generated for the paper "Exact Discovery of Time Series Motifs under DTW".
What did we do? We introduced a practical tool, which we call SWAMP (Scalable Warping Aware Matrix Profile ), to find DTW-based motifs in large datasets. 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.
Why use DTW? In many domains, DTW-based motifs represent semantically correct conserved behavior that would escape our attention using all existing Euclidean distance-based methods. Here are some examples:
The expanded version of the paper which includes all the details, experiments and case studies can be found here.
All datasets used for this paper (with the reference to the figures in the paper) are available in the following links :
All codes in this project have been implemented in Matlab, and are accessible via the following link:
Large scale versions of the figures in the paper are available in the following PowerPoint slides:
Additional tests are provided in the following subpages: