Summary of Domain Adaptation For Time Series Transformers Using One-step Fine-tuning, by Subina Khanal et al.
Domain Adaptation for Time series Transformers using One-step fine-tuning
by Subina Khanal, Seshu Tirupathi, Giulio Zizzo, Ambrish Rawat, Torben Bach Pedersen
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The recent breakthrough of Transformers in deep learning has significant implications for time series prediction. While Transformers excel at capturing long-range dependencies, they still face limitations, including insufficient temporal understanding, generalization challenges, and data shift issues when dealing with domains with limited data. Additionally, addressing catastrophic forgetting, where models forget previously learned information when exposed to new data, is crucial for enhancing the robustness of Transformers for time series tasks. To address these limitations, this paper proposes a novel approach that pre-trains a time series Transformer model on a source domain with sufficient data and fine-tunes it on the target domain with limited data. The proposed One-step fine-tuning approach involves adding some percentage of source domain data to the target domains, providing the model with diverse time series instances. A gradual unfreezing technique is also employed during fine-tuning to enhance performance. Experimental results on two real-world datasets demonstrate that this approach improves over state-of-the-art baselines by 4.35% and 11.54% for indoor temperature and wind power prediction, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers have become superstars in the world of deep learning, but they still struggle with time series prediction. They’re great at understanding patterns that happen a long time ago, but they often forget what they learned earlier on. This can be a big problem when trying to predict things like temperature or wind power. The solution proposed by this paper is to teach the Transformer model on one type of data and then fine-tune it for another type of data. This helps the model remember what it learned earlier, making its predictions much more accurate. |
Keywords
* Artificial intelligence * Deep learning * Fine tuning * Generalization * Temperature * Time series * Transformer
