PRE: An In-Context Residual Estimation Paradigm for Plug-and-Play Time-Series Forecast Correction

Time-series foundation models (TSFMs) are gaining popularity for their strong zero-shot forecasting capabilities and ease of deployment. However, recurring task-specific errors and distribution shifts create a need for adaptation during deployment, as frequent fine-tuning can be costly and requires careful hyperparameter selection and domain expertise. To address this challenge, we propose Pretrained Residual Estimator (PRE), which introduces a plug-and-play in-context learning (ICL) paradigm for post-hoc forecast correction. By conditioning on labeled historical examples, PRE infers residual corrections without task-specific online parameter updates. To support large-scale ICL pretraining, we synthesize diverse residuals and propose a two-stage pretraining strategy, first training PRE on synthetic residuals and then refining it on real residuals. PRE is independent of the base forecaster's internal architecture and supports variable context lengths and forecast horizons. Extensive evaluations demonstrate that PRE consistently improves the forecasting performance of unseen TSFMs on unseen tasks during its training and substantially outperforms supervised residual adapters, highlighting the potential of ICL as a generalizable paradigm for post-hoc forecast correction.