Time-series forecasting is a critical task in various business domains, but it remains inherently challenging. Typically, large forecasting models are trained in a single, resource-intensive run. Once training is completed, a natural question arises:~\emph{is there still potential for meaningful improvement in the model's performance?} Motivated by techniques from boosting, we introduce the concept of~\emph{post-training corrections}. This approach enhances a trained forecaster by sequentially applying a carefully selected set of corrections to its predictions. Our method offers a lightweight, model-agnostic, and scalable strategy to improve forecasting performance in practical settings. We provide theoretical foundations for the approach, starting with the affine correction case, and analyze the expected performance gains and computational costs in more general settings. Across a range of benchmark datasets, our method consistently delivers up to a $30\%$ improvement in forecasting accuracy over existing state-of-the-art models, with minimal computational overhead.