提升时间序列生成中条件变量的一致性，让预测更符合实际条件。
Semantically-Guided Inference for Conditional Diffusion Models: Enhancing Covariate Consistency in Time Series Forecasting
- 引入评分网络评估生成过程与未来条件的语义匹配度。
- 通过逐步重加权采样路径，显著提升条件一致性，准确率更高。
- 无需修改训练过程，适配各类扩散模型，尤其适合复杂场景。
扩散模型在时间序列预测中表现优异，但在复杂或多重模式条件下常出现生成轨迹与条件协变量语义不一致的问题。为此，我们提出SemGuide，一种即插即用的推理阶段方法，用于增强条件扩散模型中的协变量一致性。该方法引入评分网络，评估中间扩散状态与未来协变量之间的语义对齐程度，将得分作为每步重要性重加权的代理似然，从而在不改变原始训练流程的前提下，逐步调整采样路径。该方法具有模型无关性，兼容任意条件扩散框架。在真实世界预测任务上的实验表明，该方法在预测精度和协变量一致性上均实现持续提升，尤其在复杂条件场景下表现突出。
原文摘要 · Abstract (English)
Diffusion models have demonstrated strong performance in time series forecasting, yet often suffer from semantic misalignment between generated trajectories and conditioning covariates, especially under complex or multimodal conditions. To address this issue, we propose SemGuide, a plug-and-play, inference-time method that enhances covariate consistency in conditional diffusion models. Our approach introduces a scoring network to assess the semantic alignment between intermediate diffusion states and future covariates. These scores serve as proxy likelihoods in a stepwise importance reweighting procedure, which progressively adjusts the sampling path without altering the original training process. The method is model-agnostic and compatible with any conditional diffusion framework. Experiments on real-world forecasting tasks show consistent gains in both predictive accuracy and covariate alignment, with especially strong performance under complex conditioning scenarios.
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