TEFL: Prediction-Residual-Guided Rolling Forecasting for Multi-Horizon Time Series
arXiv:2602.22520v1 Announce Type: new Abstract: 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 proced
arXiv:2602.22520v1 Announce Type: new Abstract: 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.
Executive Summary
This article proposes TEFL, a unified learning framework that incorporates historical prediction residuals from rolling forecasts to improve the accuracy of deep multi-step time series forecasting models. By addressing key challenges such as observable multi-step residual selection, lightweight low-rank adapter integration, and two-stage training procedure design, TEFL demonstrates consistent accuracy improvement across 10 real-world datasets and 5 backbone architectures. The framework's robustness under abrupt changes and distribution shifts is also highlighted. TEFL offers a simple, general, and effective enhancement to modern deep forecasting systems, leveraging residual-based feedback directly into the learning process.
Key Points
- ▸ TEFL is a unified learning framework that incorporates historical prediction residuals from rolling forecasts.
- ▸ TEFL addresses three key challenges: observable multi-step residual selection, lightweight low-rank adapter integration, and two-stage training procedure design.
- ▸ TEFL demonstrates consistent accuracy improvement across 10 real-world datasets and 5 backbone architectures.
Merits
Strength in Adaptability
TEFL's ability to adapt to different backbone architectures and datasets showcases its flexibility and versatility.
Improved Accuracy
TEFL's consistent accuracy improvement across various datasets and architectures demonstrates its effectiveness in enhancing forecasting models.
Robustness to Changes
TEFL's robustness under abrupt changes and distribution shifts highlights its ability to handle complex and dynamic forecasting scenarios.
Demerits
Computational Complexity
The additional computational complexity introduced by TEFL's two-stage training procedure and residual-based feedback may be a limitation for resource-constrained systems.
Overfitting Risk
The use of a lightweight low-rank adapter to prevent overfitting may not be sufficient in all scenarios, potentially leading to overfitting under certain conditions.
Expert Commentary
The TEFL framework presents a promising approach to enhancing the accuracy and robustness of deep multi-step time series forecasting models. By incorporating historical prediction residuals from rolling forecasts, TEFL leverages residual-based feedback directly into the learning process, offering a simple, general, and effective enhancement to modern deep forecasting systems. The framework's ability to adapt to different backbone architectures and datasets, as well as its robustness under abrupt changes and distribution shifts, are particularly noteworthy. However, the additional computational complexity and overfitting risk introduced by TEFL's two-stage training procedure and residual-based feedback are potential limitations that warrant further investigation. As the field of time series forecasting continues to evolve, TEFL's potential to improve forecasting models in critical domains makes it an important contribution to the literature.
Recommendations
- ✓ Future research should focus on exploring the potential of TEFL in more complex forecasting scenarios, such as multi-step forecasting with multiple variables.
- ✓ Investigating the explainability of TEFL's residual-based feedback and its potential applications in other domains is recommended.
