Sub-Sequence-Based Dynamic Time Warping

Mohammed Alshehri, Frans Coenen, Keith Dures

Abstract

In time series classification the most commonly used approach is k Nearest Neighbor classification, where k = 1, coupled with Dynamic Time Warping (DTW) similarity checking. A challenge is that the DTW process is computationally expensive. This paper presents a new approach for speeding-up the DTW process, Sub-Sequence-Based DTW, which offers the additional benefit of improving accuracy. This paper also presents an analysis of the impact of the Sub-Sequence-Based method in terms of efficiency and effectiveness in comparison with standard DTW and the Sakoe-Chiba Band technique.

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