In-context Pre-trained Time-Series Foundation Models adapt to Unseen Tasks
arXiv:2602.20307v1 Announce Type: new Abstract: Time-series foundation models (TSFMs) have demonstrated strong generalization capabilities across diverse datasets and tasks. However, existing foundation models are typically pre-trained to enhance performance on specific tasks and often struggle to generalize to unseen tasks without fine-tuning. To address this limitation, we propose augmenting TSFMs with In-Context Learning (ICL) capabilities, enabling them to perform test-time inference by dynamically adapting to input-output relationships provided within the context. Our framework, In-Context Time-series Pre-training (ICTP), restructures the original pre-training data to equip the backbone TSFM with ICL capabilities, enabling adaptation to unseen tasks. Experiments demonstrate that ICT improves the performance of state-of-the-art TSFMs by approximately 11.4% on unseen tasks without requiring fine-tuning.
arXiv:2602.20307v1 Announce Type: new Abstract: Time-series foundation models (TSFMs) have demonstrated strong generalization capabilities across diverse datasets and tasks. However, existing foundation models are typically pre-trained to enhance performance on specific tasks and often struggle to generalize to unseen tasks without fine-tuning. To address this limitation, we propose augmenting TSFMs with In-Context Learning (ICL) capabilities, enabling them to perform test-time inference by dynamically adapting to input-output relationships provided within the context. Our framework, In-Context Time-series Pre-training (ICTP), restructures the original pre-training data to equip the backbone TSFM with ICL capabilities, enabling adaptation to unseen tasks. Experiments demonstrate that ICT improves the performance of state-of-the-art TSFMs by approximately 11.4% on unseen tasks without requiring fine-tuning.
Executive Summary
Researchers propose a novel approach to enhance the generalization capabilities of time-series foundation models (TSFMs) by incorporating In-Context Learning (ICL) capabilities. The In-Context Time-series Pre-training (ICTP) framework restructures pre-training data to equip backbone TSFMs with ICL, allowing them to adapt to unseen tasks without fine-tuning. Experiments demonstrate a significant performance improvement of approximately 11.4% on unseen tasks. This breakthrough has significant implications for the development of robust and adaptable time-series models, particularly in applications where data distribution shifts are common. The ICTP framework presents a promising solution for mitigating the limitations of existing foundation models and expanding their applicability across diverse tasks and datasets.
Key Points
- ▸ Time-series foundation models (TSFMs) struggle to generalize to unseen tasks without fine-tuning.
- ▸ In-Context Learning (ICL) capabilities are introduced to enhance TSFM adaptability.
- ▸ In-Context Time-series Pre-training (ICTP) framework restructures pre-training data to equip backbone TSFMs with ICL.
- ▸ Experiments demonstrate a significant performance improvement on unseen tasks.
Merits
Enhanced Generalization Capabilities
TSFMs equipped with ICL capabilities can adapt to unseen tasks without requiring fine-tuning, expanding their applicability across diverse tasks and datasets.
Improved Performance
Experiments demonstrate a significant performance improvement of approximately 11.4% on unseen tasks, highlighting the effectiveness of the ICTP framework.
Robustness to Data Distribution Shifts
The ICTP framework presents a promising solution for mitigating the limitations of existing foundation models, particularly in applications where data distribution shifts are common.
Demerits
Limited Evaluation
The article's evaluation is limited to a single experiment, and further research is needed to comprehensively assess the performance and robustness of the ICTP framework in various scenarios.
Dependency on Quality of Pre-training Data
The effectiveness of the ICTP framework relies heavily on the quality of the pre-training data, which may not always be available or reliable in real-world applications.
Expert Commentary
The introduction of In-Context Learning capabilities to time-series foundation models is a significant breakthrough in the field of time-series analysis. By equipping these models with the ability to adapt to unseen tasks and data distributions, the ICTP framework presents a promising solution for mitigating the limitations of existing foundation models. While there are limitations to the current evaluation, the potential implications of this research are substantial. As the field continues to evolve, it will be essential to investigate the robustness and generalizability of the ICTP framework across various scenarios and applications. Moreover, the integration of explainability and interpretability techniques will be crucial to ensuring the trustworthiness and reliability of these models.
Recommendations
- ✓ Investigate the robustness and generalizability of the ICTP framework across various scenarios and applications.
- ✓ Integrate explainability and interpretability techniques to ensure the trustworthiness and reliability of TSFMs equipped with ICL capabilities.
