Time Series Imputation

Follow this topic Ask about this topic

Momentum

6 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 39

All topics

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning
Apr 25, 2026Wugeng Zheng, Ziwen Kan, Katie Wang +2Multimodal Federated Learning Time Series Imputation
University of Central Florida · Independent Researcher
EVIL: Evolving Interpretable Algorithms for Zero-Shot Inference on Event Sequences and Time Series with LLMs
Apr 17, 2026David BerghausEvent Time Prediction Time Series Imputation
Lamarr Institute · Fraunhofer IAIS
Modeling Information Blackouts in Missing Not-At-Random Time Series Data
Jan 4, 2026Aman Sunesh, Allan Ma, Siddarth NilolTime Series Imputation Incomplete Data Imputation
New York University
FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation
Dec 17, 2025Runze Li, Hanchen Wang, Wenjie Zhang +4Time Series Imputation Incomplete Data Imputation
University of New South Wales, Australia · University of Technology Sydney, Australia · The Chinese University of Hong Kong, Shenzhen, China
HAGI++: Head-Assisted Gaze Imputation and Generation
Nov 4, 2025Chuhan Jiao, Zhiming Hu, Andreas BullingGaze Estimation Time Series Imputation
University of Stuttgart, Germany · The Hong Kong University of Science and Technology (Guangzhou), China
Temporal Variational Implicit Neural Representations
Jun 2, 2025Batuhan Koyuncu, Rachael DeVries, Ole Winther +1Irregular Time-Series Modeling Multivariate Time Series Forecasting
Department of Computer Science, Saarland University · Department of Biology, University of Copenhagen · Novo Nordisk A/S +1
Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data
Feb 22, 2024YongKyung Oh, Dong-Young Lim, Sungil KimIrregular Time-Series Modeling Time Series Imputation
Ulsan National Institute of Science and Technology, Republic of Korea
DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series Analysis
Jan 10, 2024YongKyung Oh, Dong-Young Lim, Sungil KimIrregular Time-Series Modeling Time Series Imputation
Medical & Imaging Informatics (MII) Group, University of California, Los Angeles (UCLA), CA, USA · Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST), Republic of Korea · Artificial Intelligence Graduate School, Ulsan National Institute of Science and Technology (UNIST), Republic of Korea
SafeImpute: Reliable Clinical Data Imputation via Conformal Selection
Date pendingXinrui He, Mengting Ai, Junting Wang +2Time Series Imputation Incomplete Data Imputation
University of Illinois Urbana-Champaign Champaign, IL, USA · Mayo Clinic Scottsdale, AZ, USA

Apr 25, 2026, cs.LGJ/K move · Enter open · S save

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning

Wugeng Zheng, Ziwen Kan, Katie Wang, Chen Chen+1

University of Central Florida · Independent Researcher

Multimodal Federated Learning Time Series Imputation Incomplete Data Imputation Learning with Missing Data Federated Learning

Open paper AskPDF arXiv

Multimodal Federated Learning (MMFL) enables privacy-preserving collaborative training, but real-world clinical applications often suffer from within-modality missingness caused by sensor intermittency or irregular sampling. Existing methods implicitly represent unobserved data via architectural alignment or missing embeddings, often failing to recover the true distribution and yielding sub-optimal performance. We propose CondI, a federated framework explicitly addressing this missingness using conditional diffusion models. CondI employs a two-phase training pipeline: first, imputing unobserved temporal components using available multimodal context and conditional embeddings; second, optimizing modality-specific extractors and joint embedding spaces. During inference, imputed raw data pass through trained extractors to generate robust features, providing a holistic representation for downstream tasks. Explicit data imputation ensures models operate on complete semantic structures, significantly enhancing resilience against severe data incompleteness. Experiments on three clinical datasets (PTB-XL, SLEEP-EDF, MIMIC-IV) demonstrate CondI achieves comparable results to state-of-the-art baselines. Code: https://github.com/ZhengWugeng/CondI

Momentum

Related topics

Latest papers 39