时间序列预测的长期误差累积是实际应用中的痛点，做时序预测的团队可以直接用UEC-STD提升现有模型性能，无需重新训练，值得一试。
现代深度学习模型在时间序列预测中表现优异，但在长期预测中因自回归推理的误差累积导致性能下降。经典误差校正机制（ECM）在统计方法中有效，但在深度学习中应用有限。本文提出一种架构无关的通用误差校正器UEC-STD，通过将预测分解为趋势和季节成分分别校正，显著提升校正精度和鲁棒性。该方法无需重新训练即可集成到现有预测器中，在4种骨干网络和10个数据集上验证了有效性。代码已开源，为缓解深度时间序列模型的自回归误差提供了实用工具。
Reviving Error Correction in Modern Deep Time-Series Forecasting
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.