RDDMPI: Residual Denoising Diffusion Model for Probabilistic Multivariate Time Series Imputation — Paper

Abstract

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.

Authors 3

Ramiro Valdes Jara, David Chapman, Adam Meyers

Specification

Official page: arxiv.org/abs/2609.11648
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Arxiv announce type: new
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
arXiv id: 2609.11648
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Categories: cs.LG, stat.ML
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
PDF: arxiv.org/pdf/2609.11648
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Primary category: cs.LG
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh

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Authors: Ramiro Valdes Jara, David Chapman, Adam Meyers

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Value	Valid from → to	Status	Source	Confidence	Extractor
https://arxiv.org/abs/2609.11648	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperRDDMPI: Residual Denoising Diffusion Model for Probabilistic Multivariate Time Series Imputation
New paper: RDDMPI: Residual Denoising Diffusion Model for Probabilistic Multivariate Time Series Imputation
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arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	1 h ago	1

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