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Proceedings of the 2018 SIAM International Conference on Data Mining

Efficient search of the best warping window for Dynamic Time Warping


Time series classification maps time series to labels. The nearest neighbor algorithm (NN) using the Dynamic Time Warping (DTW) similarity measure is a leading algorithm for this task and a component of the current best ensemble classifiers for time series. However, NN-DTW is only a winning combination when its meta-parameter – its warping window – is learned from the training data. The warping window (WW) intuitively controls the amount of distortion allowed when comparing a pair of time series. With a training database of N time series of lengths L, a naive approach to learning the WW requires Θ(N2·L3) operations. This often results in NN-DTW requiring days for training on datasets containing a few thousand time series only. In this paper, we introduce FastWWSearch: an efficient and exact method to learn WW. We show on 86 datasets that our method is always faster than the state of the art, with at least one order of magnitude and up to 1000x speed-up.

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cover image Proceedings
Proceedings of the 2018 SIAM International Conference on Data Mining
Pages: 225 - 233
Editors: Martin Ester, Simon Fraser University, Canada and Dino Pedreschi, University of Pisa, Italy
ISBN (Online): 978-1-61197-532-1


Published online: 7 May 2018



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