Reviving Error Correction in Modern Deep Time-Series Forecasting

2026-05-20

AI Summary

This work addresses the significant performance degradation of deep time series models in long-term forecasting, which stems from error accumulation inherent in autoregressive inference. To mitigate this issue, the authors propose UEC-STD—a universal, plug-and-play error corrector that requires no retraining and, for the first time, effectively adapts classical error correction mechanisms to modern deep architectures. By decomposing the time series into seasonal and trend components and applying modular correction to each, UEC-STD operates in an architecture-agnostic manner and integrates seamlessly into existing models. Extensive experiments across four mainstream backbone models and ten benchmark datasets demonstrate that UEC-STD substantially enhances both accuracy and robustness in long-term forecasting tasks.

Abstract

Modern deep-learning models have achieved remarkable success in time-series forecasting. Yet, their performance degrades in long-term prediction due to error accumulation in autoregressive inference, where predictions are recursively used as inputs. While classical error correction mechanisms (ECMs) have long been used in statistical methods, their applicability to deep learning models remains limited or ineffective. In this work, we revisit the error accumulation problem in deep time-series forecasting and investigate the role and necessity of ECMs in this new context. We propose a simple, architecture-agnostic error correction model that can be integrated with any existing forecaster without requiring retraining. By explicitly decomposing predictions into trend and seasonal components and training the corrector to adjust each separately, we introduce the Universal Error Corrector with Seasonal-Trend Decomposition (UEC-STD), which significantly improves correction accuracy and robustness across 4 backbones and 10 datasets. Our findings provide a practical tool for enhancing forecasts while offering new insights into mitigating autoregressive errors in deep time-series models. Code is available at https://github.com/DA2I2-SLM/UEC-STD.

Problem

• error accumulation
• time-series forecasting
• autoregressive inference
• error correction
• deep learning

Innovation

• error correction
• time-series forecasting
• autoregressive error
• seasonal-trend decomposition
• architecture-agnostic