A Shapelet Transform for Multivariate Time Series Classification
Not on the map. Why?
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.
Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). First posted on arXiv 18 Dec 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 2.
3 papers.
- 2024Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classificationcited by 12
- 2024Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classificationcited by 7
- 2021Mimic: An adaptive algorithm for multivariate time series classificationcited by 0
- 2024Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classification1 Nov 2024
- 2024Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification24 Oct 2024
- 2021Mimic: An adaptive algorithm for multivariate time series classification8 Nov 2021