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

TL;DR

This paper introduces a novel shapelet-based method for time series classification that incorporates dilation and new features, improving accuracy while maintaining interpretability and scalability.

Contribution

It presents a new formulation of shapelets with dilation and a novel feature, enhancing discriminative power and outperforming traditional shapelet algorithms.

Findings

Improves accuracy over existing shapelet methods

Achieves comparable results to recent state-of-the-art approaches

Maintains scalability and interpretability

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.