SparseDTW: a novel approach to speed up dynamic time warping

Authors:
Ghazi Al-Naymat;Sanjay Chawla;Javid Taheri
Affiliations:
The University of New South Wales Sydney, NSW, Australia;The University of Sydney, Australia;The University of Sydney, Australia
Venue:
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
Year:
2009

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Abstract

We present a new space-efficient approach, (SparseDTW), to compute the Dynamic Time Warping (DTW) distance between two time series that always yields the optimal result. This is in contrast to other known approaches which typically sacrifice optimality to attain space efficiency. The main idea behind our approach is to dynamically exploit the existence of similarity and/or correlation between the time series. The more the similarity between the time series the less space required to compute the DTW between them. To the best of our knowledge, all other techniques to speedup DTW, impose apriori constraints and do not exploit similarity characteristics that may be present in the data. We conduct experiments and demonstrate that SparseDTW outperforms previous approaches.