Irregular Time-Series Modeling

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Irregular time-series modeling studies how to forecast, classify, reconstruct, and generate sequences when observations are unevenly spaced, asynchronous, or missing—a common feature of clinical, scientific, and sensor data. Approaches include continuous-time neural ODEs, SDEs, and controlled differential equations; event-based and attention models; and probabilistic methods such as Gaussian processes, normalizing flows, and diffusion models. A central challenge is deciding how to represent observation times and missingness, which may carry predictive information but can also create shortcuts when sampling patterns change. Benchmarks and metrics increasingly test robustness to sparsity and sampling shifts, and results suggest no single modeling strategy consistently works best across settings.

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103 papers11 papers in the last four weeks, up 22% on the four weeks before. 0.1% of all new papers.

MM-ISTS: Cooperating Irregularly Sampled Time Series Forecasting with Multimodal Vision-Text LLMs
Mar 6, 2026Zhi Lei, Chenxi Liu, Hao Miao +3Irregular Time-Series Modeling Multivariate Time Series Forecasting
East China Normal University, Shanghai, China · Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong, China · Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China
SELDON: Supernova Explosions Learned by Deep ODE Networks
Mar 4, 2026Jiezhong Wu, Jack O'Brien, Jennifer Li +6Astrophysics Irregular Time-Series Modeling
NSF-Simons AI Institute for the Sky (SkAI), Chicago, IL, USA · Department of Industrial Engineering and Management Sciences, Northwestern University · Department of Astronomy, University of Illinois Urbana-Champaign +4
Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting
Feb 25, 2026Boyuan Li, Zhen Liu, Yicheng Luo +1Irregular Time-Series Modeling Multivariate Time Series Forecasting
School of Computer Science and Engineering, South China University of Technology
A Statistical Approach for Modeling Irregular Multivariate Time Series with Missing Observations
Feb 23, 2026Dingyi Nie, Yixing Wu, C. -C. Jay KuoIrregular Time-Series Modeling Multivariate Time Series Classification
University of Southern California
Structure-Aware Set Transformers: Temporal and Variable-Type Attention Biases for Asynchronous Clinical Time Series
Feb 18, 2026Joohyung Lee, Kwanhyung Lee, Changhun Kim +1Set Transformer Irregular Time-Series Modeling
AITRICS · Korea Advanced Institute of Science and Technology (KAIST)
Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates
Feb 4, 2026Irene Iele, Giulia Romoli, Daniele Molino +4Irregular Time-Series Modeling Multivariate Time Series Forecasting
Unit of Artificial Intelligence and Computer Systems, Universit`a Campus Bio-Medico di Roma, Italy · Department of Diagnostics and Intervention, Biomedical Engineering and Radiation Physics, Ume˚a University, Sweden · Department of Naval, Electrical, Electronics and Telecommunications Engineering, University of Genoa, Italy
Diff-MN: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observations
Jan 20, 2026Xu Zhang, Junwei Deng, Chang Xu +2Irregular Time-Series Modeling Neural Controlled Differential Equations
Microsoft Research, Beijing, China · University of Illinois Urbana-Champaign, Illinois, United States · Imperial College London, London, United Kingdom
FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classification
Nov 13, 2025YongKyung Oh, Dong-Young Lim, Sungil KimIrregular Time-Series Modeling Time Series Classification
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
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
A Jump-Diffusion Framework for Irregular Time Series Generation
May 29, 2025O. Pfohl, J. Chemseddine, P. Hagemann +3Irregular Time-Series Modeling Stochastic Differential Equations
Department of Mathematics, Humboldt-Universität zu Berlin, 12489, Berlin, Germany · Department of Mathematics, Technische Universität Berlin, 10623, Berlin, Germany · Bundesanstalt für Materialforschung und -prüfung, 12205, Berlin, Germany +1
Spectral structure learning for clinical time series
Feb 17, 2025Ivan Lerner, Anita Burgun, Francis BachMulti-Output Gaussian Processes Time-Series Causal Discovery
Université Paris Cité, Inria, Inserm, HeKA, F-75015 Paris, France · Service d’informatique biomédicale, AP-HP, Hôpital Georges Pompidou, F-75015 Paris, France · SIERRA, Inria Paris, F-75015 Paris, France
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

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MM-ISTS: Cooperating Irregularly Sampled Time Series Forecasting with Multimodal Vision-Text LLMs

Zhi Lei, Chenxi Liu, Hao Miao, Wanghui Qiu+2

East China Normal University, Shanghai, China · Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong, China · Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China

Irregular Time-Series Modeling Multivariate Time Series Forecasting LLM-Based Time Series Forecasting Multimodal Learning

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Irregularly sampled time series (ISTS) are widespread in real-world scenarios, exhibiting asynchronous observations on uneven time intervals across diverse variables. Existing ISTS forecasting methods often solely utilize historical observations to predict future ones while falling short in learning contextual semantics and fine-grained temporal patterns. To address these problems, we propose MM-ISTS, a multimodal ISTS forecasting framework augmented by vision-text large language models, which bridges temporal, visual, and textual modalities. MM-ISTS encompasses a two-stage encoding mechanism. In particular, a Cross-Modal Vision-Text Encoding module is proposed to automatically generate informative visual images and textual data, enabling the capture of intricate temporal patterns and comprehensive contextual understanding, in collaboration with multimodal LLMs (MLLMs). In parallel, ISTS encoding extracts complementary yet enriched temporal features from historical ISTS observations, including multi-view embedding fusion and a Temporal-Variable Encoder. Further, we propose an Adaptive Query-Based Feature Extractor to compress MLLM token embeddings while preserving useful knowledge, which in turn reduces computational costs. In addition, a Multimodal Alignment module with Modality-Aware Gating is designed to alleviate the modality gaps. Extensive experiments on real data offer insight into the effectiveness of the proposed solutions.