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arXiv Machine Learning By Ramiro Valdes Jara, David Chapman, Adam Meyers

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

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RDDMPI introduces a residual denoising diffusion model for multivariate time series imputation. By decomposing the missing signal into a baseline reconstruction and a residual uncertainty component, the method conditions the diffusion process on both the completed signal and its latent representation, using a reliability-aware mechanism to balance baseline influence. Experiments on benchmark datasets show that this approach improves reconstruction accuracy and uncertainty quantification compared to prior diffusion-based methods.

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