Reconstruction Advance Prediction

The core objective of diverse time-series prediction methods is to learn a function that maps historical inputs to future outputs. However, the overlap between the input and output segments is often overlooked, which can be deemed the reconstruction of an overlap window. This shared portion naturally carries information about the underlying dynamics but is not explicitly exploited by many methods. Here, we propose **Re**construction **A**dvance **P**rediction (**ReAP**) as a plug-in training paradigm to explicitly leverage this overlap information. Theoretically, we show that reconstructing the overlap window can enhance prediction performance from the perspective of Dynamic Mode Decomposition (DMD). Building on this insight, we introduce a consistent loss to ensure that the learned function aligns with the real dynamics underlying the temporal system. We first demonstrate ReAP’s effectiveness with a single linear layer, then generalize the paradigm to several state-of-the-art (SOTA) models. Extensive experiments show that ReAP consistently improves prediction performance across various deep learning backbones on 7 real-world datasets. An ablation study further confirms that a suitable ReAP configuration enables the learned function to follow the scaling law of Time Series Forecasting (TSF). Code is available at this repository: https://anonymous.4open.science/r/ReAP-BB71/.