Authors:
Mohammed Alshehri
1
;
2
;
Frans Coenen
2
and
Keith Dures
2
Affiliations:
1
Department of Computer Science, King Khalid University, Abha, Saudi Arabia
;
2
Department of Computer Science, University of Liverpool, Liverpool, U.K.
Keyword(s):
Time Series Analysis, Dynamic Time Warping, K-Nearest Neighbour Classification, Sub-Sequence-Based DTW, Matrix Profile, Motifs.
Abstract:
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.