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Shapelet-based algorithms are widely used for time series classificationbecause of their ease of interpretation, but they are currently outperformed byrecent state-of-the-art approaches. We present a new formulation of time seriesshapelets including the notion of dilation, and we introduce a new shapeletfeature to enhance their discriminative power for classification. Experimentsperformed on 112 datasets show that our method improves on the state-of-the-artshapelet algorithm, and achieves comparable accuracy to recent state-of-the-artapproaches, without sacrificing neither scalability, nor interpretability.

Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets