What is in-context fine-tuning and how does it benefit time-series forecasting?

In-context fine-tuning aims to recover the performance benefits of fine-tuning a time-series foundation model without actually modifying the model's weights. This is achieved by providing relevant examples from a target dataset at inference time, enabling the model to adapt to specific tasks without losing its zero-shot capabilities.