TEFL: Prediction-Residual-Guided Rolling Forecasting for Multi-Horizon Time Series
This work offers a general enhancement for modern deep time series forecasting systems, improving accuracy and robustness for practitioners in domains like transportation, energy, and meteorology.
The paper addresses the problem of improving time series forecasting by leveraging historical prediction residuals, which are often overlooked by deep forecasting models. The proposed TEFL framework explicitly incorporates these residuals during training and evaluation, leading to an average MAE reduction of 5-10% across various datasets and architectures, with reductions up to 19.5% in challenging scenarios.
Time series forecasting plays a critical role in domains such as transportation, energy, and meteorology. Despite their success, modern deep forecasting models are typically trained to minimize point-wise prediction loss without leveraging the rich information contained in past prediction residuals from rolling forecasts - residuals that reflect persistent biases, unmodeled patterns, or evolving dynamics. We propose TEFL (Temporal Error Feedback Learning), a unified learning framework that explicitly incorporates these historical residuals into the forecasting pipeline during both training and evaluation. To make this practical in deep multi-step settings, we address three key challenges: (1) selecting observable multi-step residuals under the partial observability of rolling forecasts, (2) integrating them through a lightweight low-rank adapter to preserve efficiency and prevent overfitting, and (3) designing a two-stage training procedure that jointly optimizes the base forecaster and error module. Extensive experiments across 10 real-world datasets and 5 backbone architectures show that TEFL consistently improves accuracy, reducing MAE by 5-10% on average. Moreover, it demonstrates strong robustness under abrupt changes and distribution shifts, with error reductions exceeding 10% (up to 19.5%) in challenging scenarios. By embedding residual-based feedback directly into the learning process, TEFL offers a simple, general, and effective enhancement to modern deep forecasting systems.