A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning
Summary: First unsupervised multivariate time-series URL using shapelet-based embeddings via contrastive learning. Uses a unified shapelet encoder with multi-grained contrast, multi-scale alignment and augmentations, outperforming URL and task-specific baselines on many datasets. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Zhiyu Liang (Harbin Engineering University)
- 2. Jianfeng Zhang (Huawei)
- 3. Chen Liang (Harbin Engineering University)
- 4. Hongzhi Wang (Harbin Engineering University)
- 5. Zheng Liang (Harbin Engineering University)
- 6. Lujia Pan (Huawei)
BibTeX Citation
@article{liang_vldb24,
title = {{A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning}},
author = {Liang, Zhiyu and Zhang, Jianfeng and Liang, Chen and Wang, Hongzhi and Liang, Zheng and Pan, Lujia},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {3},
pages = {386--399},
doi = {10.14778/3632093.3632103},
url = {https://doi.org/10.14778/3632093.3632103},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,698 | TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis | 2024 | VLDB | 5.2351259e-05 |
| 10,722 | KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly Detection | 2025 | SIGMOD | 5.093636e-05 |
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