Time-Series Representation Learning via Temporal and Contextual Contrasting

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Time-Series Representation Learning via Temporal and Contextual Contrasting
Title:
Time-Series Representation Learning via Temporal and Contextual Contrasting
Other Titles:
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Keywords:
Publication Date:
11 August 2021
Citation:
Eldele, E., Ragab, M., Chen, Z., Wu, M., Kwoh, C. K., Li, X., & Guan, C. (2021). Time-Series Representation Learning via Temporal and Contextual Contrasting. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. doi:10.24963/ijcai.2021/324
Abstract:
Learning decent representations from unlabeled time-series data with temporal dynamics is a very challenging task. In this paper, we propose an unsupervised Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC), to learn time-series representation from unlabeled data. First, the raw time-series data are transformed into two different yet correlated views by using weak and strong augmentations. Second, we propose a novel temporal contrasting module to learn robust temporal representations by designing a tough cross-view prediction task. Last, to further learn discriminative representations, we propose a contextual contrasting module built upon the contexts from the temporal contrasting module. It attempts to maximize the similarity among different contexts of the same sample while minimizing similarity among contexts of different samples. Experiments have been carried out on three real-world time-series datasets. The results manifest that training a linear classifier on top of the features learned by our proposed TS-TCC performs comparably with the supervised training. Additionally, our proposed TS-TCC shows high efficiency in few-labeled data and transfer learning scenarios. The code is publicly available at https://github.com/emadeldeen24/TS-TCC.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - AME Programmatic Funds - Learning with Less Data
Grant Reference no. : A20H6b0151

This research / project is supported by the Agency for Science, Technology and Research - Career Development Award - Contrastive Learning for Time Series Domain Adaptation
Grant Reference no. : C210112046
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