RDDMPI：用于概率多元时间序列插补的残差去噪扩散模型

中文摘要 AI 辅助

多元时间序列插补（MTSI）旨在恢复由多个相互依赖变量组成的时间数据中的缺失值。该问题在医疗监测、交通网络和能源系统等实际应用中至关重要。近年来，基于扩散的方法通过学习通过迭代去噪生成缺失值，在概率插补方面展现出强大潜力。然而，大多数现有方法直接在原始数据空间中进行扩散，要求去噪网络同时捕捉全局结构、时间动态和随机变异性。这使得生成任务变得不必要地复杂，尤其是当现代确定性插补器已经能够提供准确的初始重建时。为解决这一局限，我们提出RDDMPI，一种直接在残差空间中运行的残差条件扩散框架。我们不是直接建模完整的缺失信号，而是将概率插补重新表述为基线-残差分解，其中预训练模型捕捉主导信号，扩散过程建模残差不确定性。为更好地利用确定性引导，该模型在反向去噪过程中同时以基线补全信号及其潜在表示为条件，而可靠性感知的条件机制在残差生成过程中自适应地控制基线信息的影响。这种表述简化了扩散学习目标，使其能够专注于结构化修正项，而非重建完整信号。在多个基准数据集上的实验表明，RDDMPI在重建准确性和不确定性量化方面均持续改进。

英文摘要

Multivariate time series imputation (MTSI) aims to recover missing values in temporal data composed of multiple interdependent variables. This problem is central to real-world applications such as healthcare monitoring, traffic networks, and energy systems. Recent diffusion-based approaches have shown strong potential for probabilistic imputation by learning to generate missing values through iterative denoising. However, most existing approaches perform diffusion directly in the original data space, requiring the denoising network to simultaneously capture global structure, temporal dynamics, and stochastic variability. This makes the generative task unnecessarily complex, especially when modern deterministic imputers can already provide accurate initial reconstructions. To address this limitation, we propose RDDMPI, a conditional residual diffusion framework that operates directly in residual space. Instead of modeling the full missing signal directly, we reformulate probabilistic imputation as a baseline-residual decomposition, where a pretrained model captures the dominant signal and a diffusion process models the residual uncertainty. To better exploit deterministic guidance, \model{} conditions the reverse denoising process on both the baseline-completed signal and its latent representation, while a reliability-aware conditioning mechanism adaptively controls the influence of baseline information during residual generation. This formulation simplifies the diffusion learning objective, enabling it to focus on structured correction terms rather than reconstructing the full signal. Experiments on multiple benchmark datasets demonstrate that RDDMPI consistently improves both reconstruction accuracy and uncertainty quantification.

发表机构

• University of Miami（迈阿密大学）

机构由 AI 辅助整理，请以论文原文为准。