Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets
Computer Vision and Pattern Recognition
2022-06-10 v2 Machine Learning
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.
Cite
@article{arxiv.2109.13514,
title = {Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets},
author = {Antoine Guillaume and Christel Vrain and Elloumi Wael},
journal= {arXiv preprint arXiv:2109.13514},
year = {2022}
}