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Communication Dans Un Congrès Année : 2018

Efficient search of the best warping window for Dynamic Time Warping

Résumé

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(N 2 •L 3) 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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Dates et versions

hal-03800575 , version 1 (06-10-2022)

Identifiants

Citer

Chang Wei Tan, Matthieu Herrmann, Germain Forestier, Geoffrey Webb, François Petitjean. Efficient search of the best warping window for Dynamic Time Warping. SIAM International Conference on Data Mining (SDM), May 2018, San Diego, United States. pp.225-233, ⟨10.1137/1.9781611975321.26⟩. ⟨hal-03800575⟩

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