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This research introduces a lightweight, model-agnostic technique called post-training corrections that boosts time-series forecasting accuracy by up to 30% with minimal computational overhead. By applying sequential corrections inspired by boosting methods, the approach offers a scalable way to refine existing models without retraining. The study provides theoretical foundations for these gains, making it a practical addition to the toolkit for deploying high-performance forecasters in resource-constrained environments.
Post-Training Corrections for Improved Time-Series Forecasting
arXiv:2505.15354v3 Announce Type: replace Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Machine Learning (stat.ML)
arXiv:2505.15354 [cs.LG]
(or arXiv:2505.15354v3 [cs.LG] for this version)
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