Candidates Reduction and Enhanced Sub-Sequence-Based Dynamic Time Warping: A Hybrid Approach



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)
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