A Systematic Evaluation of Generated Time Series and Their Effects in Self-Supervised Pretraining
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910566216368128 |
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| author | Der, Audrey Yeh, Chin-Chia Michael Dai, Xin Chen, Huiyuan Zheng, Yan Fan, Yujie Zhuang, Zhongfang Lai, Vivian Wang, Junpeng Wang, Liang Zhang, Wei Keogh, Eamonn |
| author_facet | Der, Audrey Yeh, Chin-Chia Michael Dai, Xin Chen, Huiyuan Zheng, Yan Fan, Yujie Zhuang, Zhongfang Lai, Vivian Wang, Junpeng Wang, Liang Zhang, Wei Keogh, Eamonn |
| contents | Self-supervised Pretrained Models (PTMs) have demonstrated remarkable performance in computer vision and natural language processing tasks. These successes have prompted researchers to design PTMs for time series data. In our experiments, most self-supervised time series PTMs were surpassed by simple supervised models. We hypothesize this undesired phenomenon may be caused by data scarcity. In response, we test six time series generation methods, use the generated data in pretraining in lieu of the real data, and examine the effects on classification performance. Our results indicate that replacing a real-data pretraining set with a greater volume of only generated samples produces noticeable improvement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07869 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Systematic Evaluation of Generated Time Series and Their Effects in Self-Supervised Pretraining Der, Audrey Yeh, Chin-Chia Michael Dai, Xin Chen, Huiyuan Zheng, Yan Fan, Yujie Zhuang, Zhongfang Lai, Vivian Wang, Junpeng Wang, Liang Zhang, Wei Keogh, Eamonn Machine Learning Self-supervised Pretrained Models (PTMs) have demonstrated remarkable performance in computer vision and natural language processing tasks. These successes have prompted researchers to design PTMs for time series data. In our experiments, most self-supervised time series PTMs were surpassed by simple supervised models. We hypothesize this undesired phenomenon may be caused by data scarcity. In response, we test six time series generation methods, use the generated data in pretraining in lieu of the real data, and examine the effects on classification performance. Our results indicate that replacing a real-data pretraining set with a greater volume of only generated samples produces noticeable improvement. |
| title | A Systematic Evaluation of Generated Time Series and Their Effects in Self-Supervised Pretraining |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2408.07869 |