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