Alshehri, Mohammed, Coenen, Frans 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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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) |
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| 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: | 15 Jun 2024 02:13 |
| DOI: | 10.5220/0010519301840191 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3140167 |
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