Irregular Time-Series Modeling

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11 papers in the last four weeks, up 175% on the four weeks before. 0.1% of all new papers.

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Nonlocal Bayesian Modeling of Continuous Spatio-Temporal Dynamics
Jun 12, 2026Jaeyeong Lee, Heeyoung KimIrregular Time-Series Modeling Bayesian Inference
Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
Hierarchical ODE: Learning Continuous-Time Physical Prototypes for Early Link Failure Detection
Jun 12, 2026Jiaen Lv, Leran Qi, Shaowei WangIrregular Time-Series Modeling Prototype Learning
School of Electronic Science and Engineering, Nanjing University, Nanjing, China.
DTVEM-RE: A Hierarchical Random-Effects Extension of the Differential Time-Varying Effect Model for Person-Specific Multi-Lag Estimation in Intensive Longitudinal Data
Jun 12, 2026Amartya BhattacharyaIrregular Time-Series Modeling Hierarchical Bayesian Modeling
Geisel School of Medicine, Dartmouth College
A Longitudinal Attribute-Conditioned Neural Network for Modeling Health-State Transition Probabilities in Temporally Irregular Data: The LANTERN Framework
Jun 11, 2026Bright Kwaku Manu, Beckett Sterner, Petar JevticIrregular Time-Series Modeling Healthcare
School of Computing and Augmented Intelligence, Arizona State University, Tempe, USA · School of Life Sciences, Arizona State University, Tempe, USA · School of Mathematical and Statistical Sciences, Arizona State University, Tempe, USA
Existence Precedes Value: Joint Modeling of Observational Existence and Evolving States in Time Series Forecasting
Jun 11, 2026Yifan Hu, Hongzhou Chen, Peiyuan Liu +3Irregular Time-Series Modeling Time Series Forecasting
Ant International
Multi-Rate Mixture of Experts for Accelerating Liquid Neural Network Training
Jun 10, 2026Shilong Zong, Almuatazbellah Boker, Hoda EldardiryIrregular Time-Series Modeling Liquid Neural Networks
Department of Computer Science, Virginia Tech Blacksburg, VA 24061 USA · Department of Electrical and Computer Engineering, Virginia Tech Blacksburg, VA 24061 USA
LakeFM: Toward a Foundation Model for Aquatic Ecosystems Using Irregular Multivariate Multi-depth Time Series Data
Jun 9, 2026Abhilash Neog, Sepideh Fatemi, Medha Sawhney +9Irregular Time-Series Modeling Multivariate Time Series Forecasting
Virginia Tech Blacksburg, VA, USA · Grand Valley State University Muskegon, MI, USA · University of Wisconsin - Madison Madison, WI, USA +2
Transition-Based Digital Twin Modelling for Alzheimer's Disease under Sparse Longitudinal Data
Jun 8, 2026Yinyu Huang, Yilin Zhang, Sofia Michopoulou +2Irregular Time-Series Modeling Clinical Outcome Prediction
School of Electronics and Computer Science, University of Southampton, UK · University Hospital Southampton NHS Foundation Trust, Southampton, UK · Faculty of Medicine, University of Southampton, UK
TRIAGE: Dialectical LLM Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series
Jun 8, 2026Hyeongwon Jang, Gyouk Chu, Changhun Kim +4Irregular Time-Series Modeling Logical Reasoning
KAIST · AITRICS · University of Wisconsin-Madison
TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models
Jun 4, 2026Ziwen Kan, Yishuo Chen, Kecheng Li +7Irregular Time-Series Modeling Missing-Modality Learning
Department of Computer Science, University of Central Florida · Department of Computer Science, University of North Carolina at Chapel Hill
TS-ICL: A Flexible Time-Indexed Foundation Model for Time Series via In-Context Learning
Jun 4, 2026Etienne Le Naour, Tahar Nabil, Adrien PetraliaIrregular Time-Series Modeling Time Series Imputation
1EDF R&D, Palaiseau, France
TiWeaver: Unified Temporal Dynamics Modeling via Contextual Patching
Jun 2, 2026Zhe Li, Jindong Tian, Hao Miao +3Irregular Time-Series Modeling Multivariate Time Series Forecasting
East China Normal University Shanghai, China · Hong Kong Polytechnic University Hong Kong, China · Aalborg University Aalborg, Denmark
Physically-Constrained Mamba-SDE for Remaining Useful Life Prediction under Irregular Observations
Jun 1, 2026Deyu Zhuang, Peiliang Gong, Yang Shao +4Irregular Time-Series Modeling Stochastic Differential Equations
Nanjing University of Aeronautics and Astronautics Nanjing, China · Nanyang Technological University Singapore · Singapore University of Technology and Design Singapore
GlucoFM: A Dual-Stream Foundation Model for Continuous Glucose Monitoring
May 29, 2026Zechen Li, Keerthana Natarajan, Weizhi Zhang +11Irregular Time-Series Modeling Latent Dynamics Modeling
Google Research · Work done during an internship at Google · University of New South Wales, Sydney
Faithful Embeddings of Irregular and Asynchronous Data for Online Log-NCDEs
May 28, 2026Benjamin Walker, Alexandre Bloch, Lingyi Yang +2Irregular Time-Series Modeling Neural Controlled Differential Equations
Mathematical Institute University of Oxford · School of Mathematical Sciences University of Nottingham · Department of Mathematics, Imperial College London
Online Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration
May 27, 2026Haonan Wen, Hanyang Chen, Songhe FengIrregular Time-Series Modeling Multivariate Time Series Forecasting
Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education School of Computer Science and Technology, Beijing Jiaotong University Beijing, China · School of Computer Science and Technology, Beijing Jiaotong University Beijing, China · Tangshan Research Institute, Beijing Jiaotong University Tangshan, China
QuITE: Query-Based Irregular Time Series Embedding
May 27, 2026Junghoon LimIrregular Time-Series Modeling Multivariate Time Series Forecasting
SK Shieldus, Seongnam, Republic of Korea
Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP
May 26, 2026Siddharth Chaini, Federica B. Bianco, Ashish MahabalIrregular Time-Series Modeling Neural Processes
Department of Physics and Astronomy, University of Delaware, Newark, DE 19716, USA · University of Delaware, Data Science Institute, Newark, DE 19716, USA · Joseph R. Biden, Jr. School of Public Policy and Administration, University of Delaware, DE 19716, USA +3
Towards Continuous-time Causal Foundation Models
May 26, 2026Dennis Thumm, Ruben Wiedemann, Ying ChenIrregular Time-Series Modeling Causal Foundation Models
National University of Singapore · Department of Mathematics, Centre for Quantitative Finance, Risk Management Institute, National University of Singapore. · Imperial College London
Valid and Expressive Copulas for Irregular Multivariate Time Series
May 22, 2026Christian Klötergens, Tom Hanika, Lars Schmidt-Thieme +1Irregular Time-Series Modeling Copula Models
Institute of Computer Science University of Hildesheim Hildesheim, Germany
PACD-Net: Pseudo-Augmented Contrastive Distillation for Glycemic Control Estimation from SMBG
May 20, 2026Canyu Lei, David Repaske, Jianxin XieIrregular Time-Series Modeling Contrastive Learning
University of Virginia, School of Data Science, Charlottesville, VA 22903, USA · University of Virginia, Department of Pediatrics, Charlottesville, VA 22903, USA
Latent Laplace Diffusion for Irregular Multivariate Time Series
May 19, 2026Zinuo You, Jin Zheng, John CartlidgeIrregular Time-Series Modeling Multivariate Time Series Forecasting
School of Computer Science, University of Bristol · School of Engineering Mathematics and Technology, University of Bristol
Learning to Select Source-Traceable Evidence for Language-Model Prediction from Irregular Clinical Time Series
May 19, 2026Kwanhyung Lee, Juhwan Choi, Jongheon Kim +5Irregular Time-Series Modeling Healthcare
Kim Jaechul Graduate School of AI, KAIST · AITRICS
SAGA: A Sequence-Adaptive Generative Architecture for Multi-Horizon Probabilistic Forecasting with Adaptive Temporal Conformal Prediction
May 18, 2026Gustav Olaf Yunus Laitinen-Fredriksson Lundström-Imanov, Hafize Gonca CömertIrregular Time-Series Modeling Time Series Transformers
Department of Economics, Stockholm University, SE-106 91 Stockholm, Sweden
ITGPT: Generative Pretraining on Irregular Timeseries
May 15, 2026Antoine Honoré, Ming XiaoIrregular Time-Series Modeling Self-Supervised Time Series Representation Learning
⋆Division of Information Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
SurF: A Generative Model for Multivariate Irregular Time Series Forecasting
May 13, 2026Mohammad R. Rezaei, Tejas Balaji, Rahul G. KrishnanIrregular Time-Series Modeling Multivariate Time Series Forecasting
Department of Computer Science University of Toronto Vector Institute Toronto, ON, Canada
MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling
May 13, 2026Hsing-Huan Chung, Shijun Li, Yoav Wald +3Irregular Time-Series Modeling Healthcare
University of Texas at Austin · Technion-IIT · Johns Hopkins University
One-Step Graph-Structured Neural Flows for Irregular Multivariate Time Series Classification
May 11, 2026Mengzhou Gao, Kaiwei Wang, Pengfei JiaoIrregular Time-Series Modeling Graph Structure Learning
School of Cyberspace, Hangzhou Dianzi University.
ReTAMamba: Reliability-Aware Temporal Aggregation with Mamba for Irregular Clinical Time Series Prediction
May 11, 2026Jinwoong Kim, Sangjin ParkIrregular Time-Series Modeling Clinical Outcome Prediction
Department of Industrial Data Engineering Hanyang University Seoul, Republic of Korea
TIDES: Implicit Time-Awareness in Selective State Space Models
May 10, 2026Taylan Soydan, Miguel A. Bessa, Dirk Mohr +1Irregular Time-Series Modeling Selective SSMs
AIMM, ETH Zürich Zurich, Switzerland

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Nonlocal Bayesian Modeling of Continuous Spatio-Temporal Dynamics

Jaeyeong Lee, Heeyoung Kim

Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea

Irregular Time-Series Modeling Bayesian Inference Hierarchical Bayesian Modeling Spatiotemporal Forecasting State Space Models

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Real-world spatio-temporal forecasting must handle irregular time points, spatially sparse observations, and the need for uncertainty quantification. This setting is often further compounded by nonlocal interactions (long-range spatial coupling). Modeling continuous-space, continuous-time nonlocal dynamics naturally leads to infinite-dimensional integro-differential equations (IDEs), making principled Bayesian inference intractable. We propose the NonLocal Bayesian Spatio-Temporal model (NLBST), a hierarchical Bayesian framework for continuous spatio-temporal fields that learns explicit nonlocal coupling while retaining tractable inference. NLBST represents the latent field via a coordinate-based spatial basis expansion and models the coefficient process with a continuous-time ODE whose learnable linear operator corresponds to a Galerkin reduction of a nonlocal IDE; a Neural ODE residual captures additional nonlinear dynamics. A linear-Gaussian observation model enables Kalman-style sequential updates under missing and irregular observations, while the spatial basis representation enables inductive prediction at unmeasured locations without retraining. Global parameters are learned via variational inference, and uncertainty is handled through a Bayesian hierarchy. Experiments on synthetic and real-world datasets demonstrate strong forecasting and spatial generalization with well-calibrated uncertainty, yielding substantial gains over baselines in strongly nonlocal and partially observed regimes.

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