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Life sciences · Preprint
arXiv · September 10, 2026
Posted before peer review. The findings may change or fail to hold.
This is an unpeer-reviewed preprint introducing RDDMPI, a residual diffusion-based method for imputing missing values in multivariate time series. The authors report improvements in reconstruction accuracy and uncertainty quantification on benchmark datasets, but the work has not undergone peer review and provides no clinical validation or real-world deployment evidence.
Preprint. Intervention: RDDMPI: a conditional residual diffusion framework for multivariate time series imputation operating in residual space with reliability-aware conditioning mechanism.
RDDMPI reformulates probabilistic imputation as baseline-residual decomposition to simplify the diffusion learning objective Method conditions the reverse denoising process on both baseline-completed signal and latent representation with reliability-aware conditioning Experiments on multiple benchmark datasets demonstrate consistent improvements in reconstruction accuracy and uncertainty quantification
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This is an unpeer-reviewed computational methods paper describing a novel algorithm for time series imputation; it reports experimental results on benchmarks but lacks clinical validation, prospective testing, or peer review.
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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.
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