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

Alshehri, Mohammed, Coenen, Frans ORCID: 0000-0003-1026-6649 and Dures, Keith (2021) Motif-based Classification using Enhanced Sub-Sequence-Based Dynamic Time Warping. In: 10th International Conference on Data Science, Technology and Applications, 2021-7-6 - 2021-7-8.

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Official URL: http://dx.doi.org/10.5220/0010519301840191

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.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	Time Series Analysis, Dynamic Time Warping, K-Nearest Neighbour Classification, Sub-Sequence-Based DTW, Matrix Profile, Motifs
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	12 Oct 2021 10:47
Last Modified:	18 Jan 2023 21:27
DOI:	10.5220/0010519301840191
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3140167