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

• URL: http://arxiv.org/abs/2609.11648v1
• Date: Thu, 10 Sep 2026 14:53:38 GMT
• Title: RDDMPI: Residual Denoising Diffusion Model for Probabilistic Multivariate Time Series Imputation
• Abstract summary: Multi-time series imputation aims to recover missing values in temporal data composed of multiple variables.<n>This problem is central to real-world applications such as healthcare monitoring, traffic networks, and energy systems.<n>Recent diffusion-based approaches have shown strong potential for probabilistic imputation by learning to generate missing values through iterative denoising.
• Score: 2.589687124983865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Impute-EM: Native Mixed-State Diffusion Models for Heterogeneous Data Imputation [71.74203645336385]
  Impute-EM alternates between imputing missing entries with the current model and refitting a diffusion backbone on completed data.<n>We characterize the update and show that the observed mask-indexed marginals match the targets at the limit.
  arXiv  Detail & Related papers  (2026-09-14T09:39:13Z)
• Iterative Refinement Diffusion for Super-Resolved Data Assimilation of Multiscale Physical Systems [11.5117887420162]
  Iterative Refinement (IR) is a learned data assimilation framework that combines perspectives of temporal priors, generative correction, and multiresolution reconstruction.<n>On the challenging 256x256 Kraichnan benchmark, IR achieves an RMSE of 0.184 and an SSIM of 0.836, outperforming spectral upsampling, one-shot diffusion super-resolution, enhanced deep super-resolution, and an autoregressive forecaster.
  arXiv  Detail & Related papers  (2026-08-13T22:45:40Z)
• Making Every Step Count: Spatio-Temporal Information Allocation for Imaging Inverse Problems [63.904793414062816]
  We introduce two complementary and training-free components, i.e., Spectrum-based Scheduling (SAS) and Measurement-Prioritized Attention (MPA)<n>SAS distributes the available NFEs over flow time according to the spectrum and logSNR geometry, thus better balancing semantic exploration and detail refinement.<n>MPA guides information toward weakly constrained regions, thereby enhancing semantic and structural fidelity.
  arXiv  Detail & Related papers  (2026-08-12T07:39:22Z)
• Breaking the Bottlenecks: Scalable Diffusion Models for 3D Molecular Generation [0.0]
  Diffusion models have emerged as a powerful class of generative models for molecular design.<n>Their use remains constrained by long sampling trajectories, variance in the reverse process, and limited structural awareness in denoising dynamics.<n>The Directly Denoising Diffusion Model mitigates these inefficiencies by replacing reverse MCMC updates with deterministic denoising step.
  arXiv  Detail & Related papers  (2026-01-13T20:09:44Z)
• Revisiting Multivariate Time Series Forecasting with Missing Values [65.30332997607141]
  Missing values are common in real-world time series.<n>Current approaches have developed an imputation-then-prediction framework that uses imputation modules to fill in missing values, followed by forecasting on the imputed data.<n>This framework overlooks a critical issue: there is no ground truth for the missing values, making the imputation process susceptible to errors that can degrade prediction accuracy.<n>We introduce Consistency-Regularized Information Bottleneck (CRIB), a novel framework built on the Information Bottleneck principle.
  arXiv  Detail & Related papers  (2025-09-27T20:57:48Z)
• Diffusion Models for Solving Inverse Problems via Posterior Sampling with Piecewise Guidance [52.705112811734566]
  A novel diffusion-based framework is introduced for solving inverse problems using a piecewise guidance scheme.<n>The proposed method is problem-agnostic and readily adaptable to a variety of inverse problems.<n>The framework achieves a reduction in inference time of (25%) for inpainting with both random and center masks, and (23%) and (24%) for (4times) and (8times) super-resolution tasks.
  arXiv  Detail & Related papers  (2025-07-22T19:35:14Z)
• ResPF: Residual Poisson Flow for Efficient and Physically Consistent Sparse-View CT Reconstruction [7.644299873269135]
  Sparse-view computed tomography (CT) is a practical solution to reduce radiation dose, but the resulting inverse problem poses significant challenges for accurate image reconstruction.<n>Recent advances in generative modeling, particularly Poisson Flow Generative Models (PFGM), enable high-fidelity image synthesis.<n>We propose Residual Poisson Flow (ResPF) Generative Models for efficient and accurate sparse-view CT reconstruction.
  arXiv  Detail & Related papers  (2025-06-06T01:43:35Z)
• Restoration Score Distillation: From Corrupted Diffusion Pretraining to One-Step High-Quality Generation [82.39763984380625]
  We propose textitRestoration Score Distillation (RSD), a principled generalization of Denoising Score Distillation (DSD)<n>RSD accommodates a broader range of corruption types, such as blurred, incomplete, or low-resolution images.<n>It consistently surpasses its teacher model across diverse restoration tasks on both natural and scientific datasets.
  arXiv  Detail & Related papers  (2025-05-19T17:21:03Z)
• PreAdaptFWI: Pretrained-Based Adaptive Residual Learning for Full-Waveform Inversion Without Dataset Dependency [8.719356558714246]
  Full-waveform inversion (FWI) is a method that utilizes seismic data to invert the physical parameters of subsurface media.<n>Due to its ill-posed nature, FWI is susceptible to getting trapped in local minima.<n>Various research efforts have attempted to combine neural networks with FWI to stabilize the inversion process.
  arXiv  Detail & Related papers  (2025-02-17T15:30:17Z)
• Latent Space Score-based Diffusion Model for Probabilistic Multivariate Time Series Imputation [6.9295879301090535]
  We propose the Latent Space Score-Based Diffusion Model (LSSDM) for probabilistic time series imputation. LSSDM achieves superior imputation performance while also providing a better explanation and uncertainty analysis of the imputation mechanism.
  arXiv  Detail & Related papers  (2024-09-13T15:32:26Z)
• Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction [75.91471250967703]
  We introduce a novel sampling framework called Steerable Conditional Diffusion.<n>This framework adapts the diffusion model, concurrently with image reconstruction, based solely on the information provided by the available measurement.<n>We achieve substantial enhancements in out-of-distribution performance across diverse imaging modalities.
  arXiv  Detail & Related papers  (2023-08-28T08:47:06Z)
• Influence Estimation and Maximization via Neural Mean-Field Dynamics [60.91291234832546]
  We propose a novel learning framework using neural mean-field (NMF) dynamics for inference and estimation problems. Our framework can simultaneously learn the structure of the diffusion network and the evolution of node infection probabilities.
  arXiv  Detail & Related papers  (2021-06-03T00:02:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.