Domain Adaptation for Time series Transformers using One-step fine-tuning

• URL: http://arxiv.org/abs/2401.06524v1
• Date: Fri, 12 Jan 2024 11:43:16 GMT
• Title: Domain Adaptation for Time series Transformers using One-step fine-tuning
• Authors: Subina Khanal, Seshu Tirupathi, Giulio Zizzo, Ambrish Rawat, and Torben Bach Pedersen
• Abstract summary: Transformers face limitations in time series prediction, including insufficient temporal understanding, generalization challenges, and data shift issues. We introduce the emphOne-step fine-tuning approach, adding some percentage of source domain data to the target domains. This helps enhance the model's performance in time series prediction for domains with limited data.
• Score: 5.299376333553137
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The recent breakthrough of Transformers in deep learning has drawn significant attention of the time series community due to their ability to capture long-range dependencies. However, like other deep learning models, Transformers face limitations in time series prediction, including insufficient temporal understanding, generalization challenges, and data shift issues for the domains with limited data. Additionally, addressing the issue of catastrophic forgetting, where models forget previously learned information when exposed to new data, is another critical aspect that requires attention in enhancing the robustness of Transformers for time series tasks. To address these limitations, in this paper, we pre-train the time series Transformer model on a source domain with sufficient data and fine-tune it on the target domain with limited data. We introduce the \emph{One-step fine-tuning} approach, adding some percentage of source domain data to the target domains, providing the model with diverse time series instances. We then fine-tune the pre-trained model using a gradual unfreezing technique. This helps enhance the model's performance in time series prediction for domains with limited data. Extensive experimental results on two real-world datasets show that our approach improves over the state-of-the-art baselines by 4.35% and 11.54% for indoor temperature and wind power prediction, respectively.

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