发表机构
Tsinghua University（清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出即插即用的RATL方法，通过检索基础预测器的历史残差并经路由组合校正，提升多元时间序列预测性能，在iTransformer等基准中表现优异且可迁移。
AI 中文摘要
检索增强生成（RAG）用检索到的外部证据补充参数模型，同样的思路对连续输出回归颇具吸引力，但当样本在输出水平、数值尺度或局部动态上存在差异时，直接复用检索到的目标值往往不够鲁棒。此外，传统预测流程通常将残差用于模型优化和误差诊断，却不会将单个历史残差示例作为可在推理时访问的记忆保留下来。针对多元时间序列预测，我们提出RATL，一种即插即用的残差检索与反馈校正方法。RATL冻结基础预测器以构建检索键，并将其历史预测残差转化为仅用于训练的特定于该基础模型的记忆。在推理时，RATL受因果可用性约束从相似历史上下文检索残差轨迹，再使用针对预测块和变量的感知集路由器选择并组合这些轨迹。实验表明，与当前上下文匹配的历史残差包含可复用的预测信息，且RATL在大多数实验设置中提升了冻结基础预测器的性能。 ablation进一步显示，学习到的路由增强了原始残差反馈，而基于验证的校正强度选择限制了残差。在真实世界基准中，我们使用iTransformer作为主要冻结基础预测器，与多个强大的预测基线对比，并测试跨骨干网络的可迁移性。结果显示，RATL在大多数情况下可进一步提升基础预测器的性能。总之，RATL将检索对象从历史目标值转变为特定于基础模型的历史预测误差，为连续输出预测中的学习反馈校正提供了一种基于残差记忆的即插即用范式。
英文摘要
Retrieval-augmented generation (RAG) complements parametric models with retrieved external evidence. The same idea is attractive for continuous-output regression, but directly reusing retrieved target values is often not robust when samples differ in output level, numerical scale, or local dynamics. Moreover, conventional forecasting pipelines generally use residuals for model optimization and error diagnosis, but do not retain individual historical residual examples as memory that can be accessed at inference time.For multivariate time-series forecasting, we propose RATL, a plug-in residual-retrieval and feedback-correction method. RATL freezes a base forecaster to construct retrieval keys and turns its historical forecast residuals into a train-only memory specific to that base model. At inference time, RATL retrieves residual trajectories from similar historical contexts subject to causal availability constraints, then uses a set-aware router operating over forecast blocks and variables to select and combine these trajectories. Experiments show that historical residuals matched to the current context contain reusable forecasting information and that RATL improves frozen base forecasters in most experimental settings. Ablations further show that learned routing strengthens raw residual feedback, while validation-based correction-strength selection limits residual over-injection.On real-world benchmarks, we use iTransformer as the primary frozen base forecaster, compare against multiple strong forecasting baselines, and test transferability across backbones. The results show that RATL can further improve base-forecaster performance in most settings.Overall, RATL shifts the retrieved object from historical target values to base-model-specific historical forecast errors, providing a plug-in, residual-memory-based paradigm for learned feedback correction in continuous-output forecasting.
