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.

翻译：时间序列预测是多个业务领域中的关键任务，但其本身仍极具挑战性。通常，大型预测模型只需进行一次资源密集型训练即可完成训练。训练完成后，自然会提出这样一个问题：模型的性能是否仍有显著提升的潜力？受提升方法技术的启发，我们引入了“训练后校正”的概念。该方法通过对其预测结果依次应用经过精心选择的校正集，来增强训练好的预测器。我们的方法提供了一种轻量级、模型无关且可扩展的策略，以在实际场景中提升预测性能。我们从仿射校正情形出发，为该方法的理论基础提供了支撑，并在更一般的情境下分析了预期性能增益和计算成本。在一系列基准数据集上，我们的方法始终能将预测准确率最多提升30%（优于现有最先进模型），且计算开销极低。