In-context Fine-tuning for Time-series Foundation Models

Abhimanyu Das, Matthew Faw, Rajat Sen, Yichen Zhou

Keywords: time-series, foundation models, zero-shot, few-shot, in-context

TL;DR: We design a time-series foundation model that can be prompted with related examples and can perform better forecasting compared to supervised models, statistical models and even fine-tuned time-series foundation models.

Abstract: Motivated by the recent success of time-series foundation models for zero-shot forecasting, we present a methodology for _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.

Primary Area: learning on time series and dynamical systems

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Submission Number: 12458