Alshehri, Mohammed, Coenen, Frans ORCID: 0000-0003-1026-6649 and Dures, Keith
(2020)
Candidates Reduction and Enhanced Sub-Sequence-Based Dynamic Time Warping: A Hybrid Approach.
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Abstract
Dynamic Time Warping (DTW) coupled with k Nearest Neighbour classification, where k= 1, is the most common classification algorithm in time series analysis. The fact that the complexity of DTW is quadratic, and therefore computationally expensive, is a disadvantage; although DTW has been shown to be more accurate than other distance measures such as Euclidean distance. This paper presents a hybrid, Euclidean and DTW time series analysis similarity metric approach to improve the performance of DTW coupled with a candidate reduction mechanism. The proposed approach results in better performance than alternative enhanced Sub-Sequence-Based DTW approaches, and the standard DTW algorithm, in terms of runtime, accuracy and F1 score.
Item Type: | Conference or Workshop Item (Unspecified) |
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Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
Depositing User: | Symplectic Admin |
Date Deposited: | 12 Oct 2021 10:49 |
Last Modified: | 18 Jan 2023 21:27 |
DOI: | 10.1007/978-3-030-63799-6_21 |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3140161 |