Spatiotemporal Forecasting

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39 papers in the last four weeks, up 290% on the four weeks before. 0.4% of all new papers.

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Step-adaptive multimodal fusion network with multi-scale cloud feature learning for ultra-short-term solar irradiance forecasting
Jun 4, 2026Jingxin Zhang Xiaoqin WangTime Series Forecasting Multi-Scale Feature Fusion
School of Automation, Southeast University, Nanjing, 210096, China
Field Validation of a Multi-Resolution ConvLSTM Framework for Retaining Wall Deformation Prediction
Jun 4, 2026Jihoon Kim, Saeyon Kim, Heejung YounRecurrent Neural Networks Spatiotemporal Forecasting
Department of Civil and Environmental Engineering, Hongik University, Seoul 04066, Republic of Korea
EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts
Jun 3, 2026Yiming Lu, Sihang Zeng, Zhengxu Tang +3Epidemic Forecasting Time Series Forecasting
Emory University · University of Washington
CausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting
Jun 3, 2026Zhaoqi Zhang, Miao Xie, Yi Li +3Causal Representation Learning Causal Inference
Nanyang Technological University Singapore, Singapore · China Agricultural University Beijing, China · Meituan Beijing, China
Data-efficient flood depth prediction through domain-aware coreset selection and tabular foundation models
Jun 3, 2026Lipai Huang, Adithi Srinath, Manas Singh +2Surrogate Modeling OOD Generalization
Urban Resilience.AI Lab, Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, USA · Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA · Resilitix Intelligence LLC, Houston, TX, USA +1
Physics-Informed Machine Learning for Short-Term Flood Prediction
Jun 2, 2026Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-BeniLong Short-Term Memory Networks Physics-Informed ML
North Carolina A&T State University · Greensboro, NC 27407, USA
DiffUNet^2: Bidirectional Conditional Diffusion for Probabilistic Scientific Spatiotemporal Modeling
Jun 2, 2026Mengdi Chu, Jiaxin Yang, Angus G. Forbes +4Diffusion Model Guidance Probabilistic Forecasting
Mengdi Chu, Jiaxin Yang, and Han-Wei Shen are with the Ohio State University. · Angus G. Forbes is with NVIDIA. · Nathan Debardeleben, Earl Lawrence, Ayan Biswas are with Los Alamos National Laboratory.
PatchSTG: Scalable Spatiotemporal Graph Transformers for Traffic Forecasting on Irregular Sensor Networks
Jun 2, 2026Jichao Li, Xuanming ShiIntelligent Transportation Systems Spatiotemporal Forecasting
Portsmouth Abbey School · CodingFuture (Shanghai) Education Technology Co., Ltd.
Learning to Refine: Spectral-Decoupled Iterative Refinement Framework for Precipitation Nowcasting
Jun 1, 2026Yunlong Zhou, Chen Zhao, Danyang Peng +2Precipitation Forecasting Spatiotemporal Forecasting
School of Intelligence Science and Technology, Nanjing University, Suzhou, 215163, China. · State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China.
AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks
May 31, 2026Zhongyue Zhang, Guangyin Jin, Yuxuan Liang +2Kernel Methods Spatiotemporal Forecasting
Sichuan University · PLA Academy of Military Science · The Hong Kong University of Science and Technology (Guangzhou)
Hybrid Probabilistic Forecasting of Under-Five Malaria Admissions in Ghana: A Gaussian Process Regression with Holt-Winters Smoothing
May 30, 2026T. Ansah-Narh, Y. Asare Afrane, J. Bremang TandohEpidemic Forecasting Time Series Forecasting
Spatiotemporal Multi-Task Graph Transformer for Trip-Level Transit Prediction
May 30, 2026Oluwaleke Yusuf, Adil Rasheed, Frank LindsethIntelligent Transportation Systems Multi-Task Learning
Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim NO-7491, Norway · Department of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim NO-7491, Norway
EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction
May 30, 2026Dahai Yu, Rongchao Xu, Lin Jiang +1Selective SSMs Time Series Forecasting
Florida State University · Tallahassee, Florida, USA
CHAM-net: A Contrastive Hierarchical Adaptive Meta-network for Robust Global Methane Flux Prediction
May 29, 2026Rongchao Dong, Yiming Sun, Shuo Chen +4Meta-Learning Spatiotemporal Forecasting
University of Pittsburgh · Purdue University · University of Colorado Boulder +3
Spatio-temporal stochastic graph-based learning for infectious disease forecasting
May 28, 2026Luz Stefani Sotomayor Valenzuela, Susanna Cramb, Darren WraithEpidemic Forecasting Probabilistic Forecasting
School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia · QUT Centre for Data Science, Queensland University of Technology, Brisbane, Australia
Beyond MSE: Improving Precipitation Nowcasting with Multi-Quantile Regression
May 28, 2026Gijs van Nieuwkoop, Siamak MehrkanoonPrecipitation Forecasting Quantile Regression
Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
From Short Histories to Long Futures: Horizon-Aware Graph Neural Networks for Long Horizon Forecasting
May 28, 2026Zesheng Liu, Maryam RahnemoonfarNeural Surrogate Modeling Long-Term Time Series Forecasting
Department of Computer Science and Engineering, Lehigh University, Bethlehem PA 18015, USA · Department of Civil and Environmental Engineering, Lehigh University, Bethlehem2026 PA 18015, USA
A Road-Conditioned Traffic Movie Prediction Network with Spatiotemporal and Structure-Consistent Learning
May 27, 2026Joshua Kofi Asamoah, Blessing Agyei Kyem, Armstrong AboahIntelligent Transportation Systems Traffic Flow Estimation
North Dakota State University, 1410 14th Avenue Offerdahl North Building, CIE 201, Fargo, 58102, North Dakota, United States
Transfer Learning using 66 Diseases for Disease Forecasting Applications
May 26, 2026Lauren J Beesley, Alexander C Murph, Dave Osthus +1Multivariate Time Series Forecasting Epidemic Forecasting
Statistics, Los Alamos National Laboratory · Computational Intelligence & Modeling, Los Alamos National Laboratory
SL-BiLEM: Structured Learnable Behavior-in-the-Loop Epidemic Modeling for Forecasting and Policy Evaluation
May 26, 2026Haochun Wang, Sendong Zhao, Jingbo Wang +3Epidemic Forecasting Distribution Shift
Faculty of Computing, Harbin Institute of Technology, Harbin, China
Recursive Flow Matching
May 26, 2026Jiahe Huang, Sihan Xu, Sharvaree Vadgama +1Neural Surrogate Modeling Flow Matching
University of California, San Diego · University of Michigan
Accelerated Spatio-Temporal Gaussian Rendering via Kinematic and Semantic Priors
May 25, 2026Denis Gridusov, Maxim Popov, Sergey Kolyubin3D Gaussian Splatting 4D Reconstruction
ITMO University, St. Petersburg, Russia
HumanFlow -- Diffusion-Driven MAV Navigation Among Humans via Tightly-Coupled Motion Tracking, Forecasting, and Control
May 25, 2026Simon Schaefer, Joshua Näf, Stefan LeuteneggerSocial Navigation Humanoid Motion Tracking
Technical University of Munich · MCML · MIRMI +1
PHGNet: Prototype-Guided Hypergraph Construction for Heterogeneous Spatiotemporal Forecasting
May 25, 2026Ruiwen Gu, Yahao Liu, Zhenyu Liu +2Graph Neural Networks Intelligent Transportation Systems
Shenzhen Ubiquitous Data Enabling Key Lab Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · School of Computer Science and Engineering University of Electronic Science and Technology of China, Chengdu, China
ADMFormer: An Adaptive-Decomposition Transformer with Time-Varying Masked Spatial Attention for Traffic Forecasting
May 25, 2026Ruiwen Gu, Qitai Tan, Yahao Liu +1Attention Mechanisms Spatiotemporal Forecasting
Shenzhen Ubiquitous Data Enabling Key Lab Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · School of Computer Science and Engineering University of Electronic Science and Technology of China, Chengdu, China
Autoregression-Free Neural Operators for Time-Dependent PDEs
May 25, 2026Jiaquan Zhang, Caiyan Qin, Haoyu Bian +7Latent Dynamics Modeling PDE Operator Learning
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China · School of Robotics and Advanced Manufacture, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China · School of Mathematical Sciences, Capital Normal University, Beijing 100048, China +3
High-fidelity Modeling of Full-scale Pressurized Water Reactor Flow Fields for Machine Learning Applications
May 23, 2026Logan A. Burnett, Hyungjun Kim, Hsien-Cheng Chou +5Neural Surrogate Modeling Spatiotemporal Forecasting
Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States · Korea Atomic Energy Research Institute, Daejeon, 34057, South Korea · Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, United States +2
Aurora Hunter: A Two-Stage Framework for Probabilistic Visibility Forecasting
May 21, 2026Zongyuan Ge, Chenwaner Zhang, Haoyang Li +4Probabilistic Forecasting Spatiotemporal Forecasting
College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao 266100, China · School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650504, People’s Republic of China · Engineering Research Center of Advanced Marine Physical Instruments and Equipment, Ministry of Education, Qingdao 266100, China +1
Mode-as-Sequence: Translating Multimodal Motion Prediction into Unified Sequential Mode Modeling
May 21, 2026Zikang Zhou, Haibo Hu, Xinhong Chen +5Probability Calibration Autonomous Driving
City University of Hong Kong · City University of Hong Kong (Dongguan) · Hon Hai Research Institute +1
Neural Negative Binomial Regression for Weekly Seismicity Forecasting: Per-Cell Dispersion Estimation and Tail Risk Assessment
May 20, 2026Alim IgilikProbabilistic Forecasting Seismology
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Step-adaptive multimodal fusion network with multi-scale cloud feature learning for ultra-short-term solar irradiance forecasting

Jingxin Zhang Xiaoqin Wang

School of Automation, Southeast University, Nanjing, 210096, China

Time Series Forecasting Multi-Scale Feature Fusion Solar Power Forecasting Spatiotemporal Forecasting Multimodal Fusion

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Ultra-short-term solar irradiance prediction is critical for photovoltaic system dispatch and power grid stability. Existing approaches suffer from three key shortcomings: single time-series models cannot capture the spatial dynamics of clouds under complex conditions, standard convolutions inadequately represent multi-scale cloud features, and fixed low-frequency compensation strategies fail to adapt to different prediction steps. To address these issues, this proposes a multi-source data fusion model for ultra-short-term irradiance prediction. The model first employs InceptionNeXt to extract multi-scale, multi-directional spatial features from ground-based cloud images. A step-adaptive low-frequency compensation unit is then introduced to dynamically modulate global low-frequency information based on the prediction step. Eventually, the enhanced image features are combined with meteorological time-series features, and a TempAttnLSTM network captures global temporal dependencies for multi-step prediction. Experiments on the public NREL dataset and practical photovoltaic stations in Shandong illustrate the effectiveness of the proposed method compared with several state-of-the-art approaches.

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