Motif-based Classification using Enhanced Sub-Sequence-Based Dynamic Time Warping

In time series analysis, Dynamic Time Warping (DTW) coupled with k Nearest Neighbour classification, where k = 1, is the most commonly used classification model. Even though DTW has a quadratic complexity, it outperforms other similarity measurements in terms of accuracy, hence its popularity. This paper presents two motif-based mechanisms directed at speeding up the DTW process in such a way that accuracy is not adversely affected: (i) the Differential Sub-Sequence Motifs (DSSM) mechanism and (ii) the Matrix Profile Sub-Sequence Motifs (MPSSM) mechanism. Both mechanisms are fully described and evaluated. The evaluation indicates that both DSSM and MPSSM can speed up the DTW process while producing a better, or at least comparable accuracy, in 90% of cases.