In-Context Fine-Tuning for Time-Series Foundation Models

• URL: http://arxiv.org/abs/2410.24087v1
• Date: Thu, 31 Oct 2024 16:20:04 GMT
• Title: In-Context Fine-Tuning for Time-Series Foundation Models
• Authors: Abhimanyu Das, Matthew Faw, Rajat Sen, Yichen Zhou,
• Abstract summary: In particular, we design a pretrained foundation model that can be prompted with multiple time-series examples. Our foundation model is specifically trained to utilize examples from multiple related time-series in its context window. We show that such a foundation model that uses in-context examples at inference time can obtain much better performance on popular forecasting benchmarks.
• Score: 18.348874079298298
• License:
• Abstract: Motivated by the recent success of time-series foundation models for zero-shot forecasting, we present a methodology for $\textit{in-context fine-tuning}$ of a time-series foundation model. In particular, we design a pretrained foundation model that can be prompted (at inference time) with multiple time-series examples, in order to forecast a target time-series into the future. Our foundation model is specifically trained to utilize examples from multiple related time-series in its context window (in addition to the history of the target time-series) to help it adapt to the specific distribution of the target domain at inference time. We show that such a foundation model that uses in-context examples at inference time can obtain much better performance on popular forecasting benchmarks compared to supervised deep learning methods, statistical models, as well as other time-series foundation models. Interestingly, our in-context fine-tuning approach even rivals the performance of a foundation model that is explicitly fine-tuned on the target domain.

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