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Semantically-Guided Inference for Conditional Diffusion Models: Enhancing Covariate Consistency in Time Series Forecasting
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本文提出一种名为SemGuide的方法,用于增强条件扩散模型中协变量的一致性,通过引入评分网络评估中间扩散状态与未来协变量的语义对齐度,提高预测准确性和协变量对齐度,尤其适用于复杂条件场景。

arXiv:2508.01761v1 Announce Type: cross Abstract: 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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条件扩散模型 语义对齐 预测准确度 协变量一致性 复杂条件
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