Effective Sub-Sequence-Based Dynamic Time Warping

Coenen, FP ORCID: 0000-0003-1026-6649, Dures, K and Alshehri, Mohamed (2019) Effective Sub-Sequence-Based Dynamic Time Warping. In: International Conference on Innovative Techniques and Applications of Artificial Intelligence.

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Official URL: http://dx.doi.org/10.1007/978-3-030-34885-4_23

Abstract

k Nearest Neighbour classification techniques, where k = 1, coupled with Dynamic Time Warping (DTW) are the most effective and most frequently used approaches for time series classification. However, because of the quadratic complexity of DTW, research efforts have been directed at methods and techniques to make the DTW process more efficient. This paper presents a new approach to efficient DTW, the Sub-Sequence-Based DTW approach. Two variations are considered, fixed length sub-sequence segmentation and fixed number sub-sequence segmentation. The reported experiments indicate that the technique improvs efficiency, compared to standard DTW, without adversely affecting effectiveness.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	Time series analysis, Dynamic Time Warping, k-Nearest neighbor classification, Splitting method
Depositing User:	Symplectic Admin
Date Deposited:	17 Sep 2019 08:12
Last Modified:	19 Jan 2023 00:26
DOI:	10.1007/978-3-030-34885-4_23
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3054852