arXiv:2609.37632v1 Announce Type: cross
Abstract: Time series imputation has progressed from statistical and deep learning approaches to diffusion-based models, which have shown strong recent perform...
By Fariza Rashid, Duc Van Le, Rahat Masood, Gustavo Batista, Aruna Seneviratne, Suranga Seneviratne
The paper introduces a new evaluation protocol for deep multivariate imputation models on wearable device data, addressing the issue of structured missingness where sensor features drop out together. Using a Garmin smartwatch dataset from an epilepsy patient, the authors generate realistic block-missing patterns from training data and show that matching the training protocol to this distribution reduces BRITS’ mean absolute error by 43%. They also extend BRITS with time‑of‑day encoding and compare it to linear interpolation and SAITS, finding that no single model dominates and that model rankings vary with evaluation design.
By Skye Goodman, Roussel Desmond Nzoyem, Leandro Junges, Peter Kissack, Yasser Qureshi, Amberly Brigden, Jeff Clark, Nawid Keshtmand
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.
By Ramiro Valdes Jara, David Chapman, Adam Meyers
TSGuard is a real‑time framework that monitors, validates, and imputes missing values in streaming time series. It uses a lightweight graph‑aware temporal imputation model combined with constraint‑aware validation, fallback estimation, and operator‑facing explanations. The system integrates detection, imputation, and domain‑aware decision making—retaining or replacing values based on physical and spatial constraints—within a broader data‑quality loop, and offers interactive tools for operators to inspect and compare imputers in real time.
By Imane Hocine, Asma Abboura, Soror Sahri, Abhijith Senthilkumar, Yacine Hakimi, Gr\'egoire Danoy
arXiv:2607. 03641v1 Announce Type: cross Abstract: The manifold hypothesis posits that high-dimensional data are concentrated near a low-dimensional embedded manifold.
By Zelong Bi, Amuchechukwu Ibenegbu, Sarat Moka
arXiv:2606. 05239v1 Announce Type: cross Abstract: Diffusion models have demonstrated strong performance in time series modeling due to their ability to progressively capture complex data distributions through iterative denoising.
By Hongfan Gao, Wangmeng Shen, Bin Yang, Jilin Hu