# A Shapelet Transform for Multivariate Time Series Classification

arXiv: https://arxiv.org/abs/1712.06428 · HTML: https://mlsanity.com/paper/1712.06428/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2017.

## Built on by (most cited first, 3 of 3)

- [Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classification](https://mlsanity.com/paper/2411.01006/) (2024)
- [Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification](https://mlsanity.com/paper/2410.18686/) (2024)
- [Mimic: An adaptive algorithm for multivariate time series classification](https://mlsanity.com/paper/2111.04273/) (2021)

## Abstract (arXiv)

Shapelets are phase independent subsequences designed for time series classification. We propose three adaptations to the Shapelet Transform (ST) to capture multivariate features in multivariate time series classification. We create a unified set of data to benchmark our work on, and compare with three other algorithms. We demonstrate that multivariate shapelets are not significantly worse than other state-of-the-art algorithms.