Time-series forecasting is crucial for businesses, but traditional methods are slow and expert-intensive. TimesFM, a zero-shot foundation model, improved this by forecasting without task-specific training. However, incorporating a few examples, known as few-shot learning, could enhance accuracy further. The standard method for this, supervised fine-tuning, reintroduces complexity.The new In-Context Fine-Tuning (ICF) approach transforms TimesFM into a few-shot learner by using continued pre-training. This teaches the model to learn from inference-time examples without further user training. The model, now TimesFM-ICF, uses a patched decoder architecture with transformer layers.To enable few-shot learning, a "common separator token" is introduced to distinguish between forecast history and in-context examples. This prevents data confusion and allows the model to learn from past patterns. The model is then pre-trained on a new dataset incorporating these separators.TimesFM-ICF was evaluated on unseen datasets, using relevant historical data as in-context examples. It demonstrated a 6.8% accuracy improvement over the base TimesFM. Crucially, TimesFM-ICF matches the performance of supervised fine-tuning without the need for additional complex training.The system also shows that more in-context examples lead to better forecasts, with a trade-off in inference time. This innovation promises more accessible and powerful forecasting, enabling businesses to deploy adaptable models without extensive ML projects. Future work aims to automate the selection of the most relevant in-context examples.