UniTS: A Universal Time Series Analysis Framework Powered by Self-Supervised Representation Learning
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866917751569776640 |
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| author | Liang, Zhiyu Liang, Chen Liang, Zheng Wang, Hongzhi Zheng, Bo |
| author_facet | Liang, Zhiyu Liang, Chen Liang, Zheng Wang, Hongzhi Zheng, Bo |
| contents | Machine learning has emerged as a powerful tool for time series analysis. Existing methods are usually customized for different analysis tasks and face challenges in tackling practical problems such as partial labeling and domain shift. To improve the performance and address the practical problems universally, we develop UniTS, a novel framework that incorporates self-supervised representation learning (or pre-training). The components of UniTS are designed using sklearn-like APIs to allow flexible extensions. We demonstrate how users can easily perform an analysis task using the user-friendly GUIs, and show the superior performance of UniTS over the traditional task-specific methods without self-supervised pre-training on five mainstream tasks and two practical settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_13804 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | UniTS: A Universal Time Series Analysis Framework Powered by Self-Supervised Representation Learning Liang, Zhiyu Liang, Chen Liang, Zheng Wang, Hongzhi Zheng, Bo Machine Learning Databases Machine learning has emerged as a powerful tool for time series analysis. Existing methods are usually customized for different analysis tasks and face challenges in tackling practical problems such as partial labeling and domain shift. To improve the performance and address the practical problems universally, we develop UniTS, a novel framework that incorporates self-supervised representation learning (or pre-training). The components of UniTS are designed using sklearn-like APIs to allow flexible extensions. We demonstrate how users can easily perform an analysis task using the user-friendly GUIs, and show the superior performance of UniTS over the traditional task-specific methods without self-supervised pre-training on five mainstream tasks and two practical settings. |
| title | UniTS: A Universal Time Series Analysis Framework Powered by Self-Supervised Representation Learning |
| topic | Machine Learning Databases |
| url | https://arxiv.org/abs/2303.13804 |