Learning DTW-Preserving Shapelets

Arnaud Lods (1) , Simon Malinowski (1) , Romain Tavenard (2, 3, 4) , Laurent Amsaleg (1)

1 LinkMedia - Creating and exploiting explicit links between multimedia fragments
2 OBELIX - Environment observation with complex imagery
3 UR2 - Université de Rennes 2
4 LETG - Rennes - Littoral, Environnement, Télédétection, Géomatique

Dynamic Time Warping (DTW) is one of the best similarity measures for time series, and it has extensively been used in retrieval, classification or mining applications. It is a costly measure, and applying it to numerous and/or very long times series is difficult in practice. Recently, Shapelet Transform (ST) proved to enable accurate supervised classification of time series. ST learns small subsequences that well discriminate classes, and transforms the time series into vectors lying in a metric space. In this paper, we adopt the ST framework in a novel way: we focus on learning, without class label information, shapelets such that Euclidean distances in the ST-space approximate well the true DTW. Our approach leads to an ubiquitous representation of time series in a metric space, where any machine learning method (supervised or unsupervised) and indexing system can operate efficiently.

Mots clés: time series; dynamic time warping; shapelets; clustering

Domaines: Méthodes et statistiques

Arnaud Lods, Simon Malinowski, Romain Tavenard, Laurent Amsaleg. Learning DTW-Preserving Shapelets. IDA 2017 - 16th International Symposium on Intelligent Data Analysis, Oct 2017, London, United Kingdom. pp.198-209, ⟨10.1007/978-3-319-68765-0_17⟩. ⟨hal-01565207v2⟩