弥合预测的最后一公里：利用条件引导流匹配增强时间序列预测
Bridging the Last Mile of Prediction: Enhancing Time Series Forecasting with Conditional Guided Flow Matching
AI总结：
提出模型无关框架CGFM，通过整合辅助预测模型输出扩展流匹配，利用残差概率结构并引入双侧条件路径与仿射路径，有效保持时间一致性并提升时间序列预测精度。
摘要：
现有的时间序列预测生成模型通常将简单先验（通常是高斯分布）转换为复杂数据分布。然而，其采样初始化独立于历史数据，阻碍了对时间依赖性的捕捉，限制了预测精度。它们也仅将残差作为优化目标，忽略了残差常表现出有意义的模式，如系统偏差或非平凡的分布结构。为解决这些问题，我们提出了Conditional Guided Flow Matching（CGFM），一种新颖的模型无关框架，通过整合辅助预测模型的输出来扩展流匹配。这使得从预测残差的概率结构中学习成为可能，利用辅助模型的预测分布作为源来降低学习难度并优化预测。CGFM将历史数据同时作为条件和引导，使用双侧条件路径（源和目标均以相同历史为条件），并采用仿射路径扩展路径空间，无需复杂机制即可避免路径交叉，保持时间一致性并增强分布对齐。跨数据集和基线的实验表明，CGFM持续优于最先进模型，推动了预测发展。
英文摘要：
Existing generative models for time series forecasting often transform simple priors (typically Gaussian) into complex data distributions. However, their sampling initialization, independent of historical data, hinders the capture of temporal dependencies, limiting predictive accuracy. They also treat residuals merely as optimization targets, ignoring that residuals often exhibit meaningful patterns like systematic biases or nontrivial distributional structures. To address these, we propose Conditional Guided Flow Matching (CGFM), a novel model-agnostic framework that extends flow matching by integrating outputs from an auxiliary predictive model. This enables learning from the probabilistic structure of prediction residuals, leveraging the auxiliary model's prediction distribution as a source to reduce learning difficulty and refine forecasts. CGFM incorporates historical data as both conditions and guidance, uses two-sided conditional paths (with source and target conditioned on the same history), and employs affine paths to expand the path space, avoiding path crossing without complex mechanisms, preserving temporal consistency, and strengthening distribution alignment. Experiments across datasets and baselines show CGFM consistently outperforms state-of-the-art models, advancing forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
