发表机构
University of Sydney; University of New South Wales（悉尼大学; 新南威尔士大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出ProCTI扩散插补框架，通过习得原型检索全局先验增强局部条件化，在随机缺失下优于强基线，并给出理论分析。
AI 中文摘要
时间序列插补已从统计和深度学习方法发展到基于扩散的模型，后者在近期表现出强劲性能。现有的基于扩散的方法通常利用当前或邻近窗口的局部上下文信息来条件化逆向过程。与此同时，全局数据集级别的结构往往保持隐式，当局部观测稀疏、含噪或缺乏代表性时，这会限制性能。为解决此问题，我们提出ProCTI，一种扩散插补框架，通过习得的原型用检索到的全局数据集级别先验来增强局部条件化。一种混合条件化机制在逆向扩散期间将这种全局上下文与局部信号整合，从而在多种缺失场景下实现更精确的重建。在多个基准数据集上的实验表明，ProCTI在随机缺失情况下整体优于强基线，同时在按属性缺失情况下保持竞争力。此外，我们使用潜在机制数据模型来刻画原型派生的全局条件化可证明改善插补的精确条件。我们以对局部-全局条件化的一般理论分析来支持这一点。
英文摘要
Time series imputation has progressed from statistical and deep learning approaches to diffusion-based models, which have shown strong recent performance. Existing diffusion-based methods typically condition the reverse process using local contextual information from the current or neighbouring windows. Meanwhile, global dataset-level structure often remains implicit, limiting performance when local observations are sparse, noisy, or unrepresentative. To address this issue, we propose ProCTI, a diffusion-imputation framework that augments local conditioning with retrieved global dataset-level priors through learned prototypes. A hybrid conditioning mechanism integrates this global context with local signals during reverse diffusion, enabling more accurate reconstruction under varying missingness scenarios. Experiments across multiple benchmark datasets show that ProCTI outperforms strong baselines overall under random missingness, while remaining competitive under attribute-wise missingness. Furthermore, we use a latent-regime data model to characterise the precise conditions under which prototype-derived global conditioning provably improves imputation. We support this with a general theoretical analysis of local-global conditioning.
