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

Code

• github.com/baraline/convst
  OfficialIn papernone★ 35

Abstract

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.

Tasks

Benchmark Results

Reproductions