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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Latest papers 279

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PACT: Peak-Aware Cross-Attention Graph Transformers for Efficient Storm-Surge Emulation
May 9, 2026Zesheng Liu, Doyup Kwon, Ning Lin +1PDE Surrogate Modeling Spatiotemporal Forecasting
Department of Computer Science and Engineering, Lehigh University · Department of Civil and Environmental Engineering, Princeton University · Department of Civil and Environmental Engineering, Lehigh University
A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting
May 9, 2026Pavan Manjunath, Thomas PruferTime Series Forecasting Quantum-Inspired ML
Independent Research Scholar, Germany
PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting
May 9, 2026Hao Wu, Fan Xu, Yuxu Lu +9Long-Term Time Series Forecasting Climate Modeling
Tsinghua University · Tencent · Shenzhen Loop Area Institute +6
Controlling Transient Amplification Improves Long-horizon Rollouts
May 9, 2026Adeel Pervez, Francesco LocatelloJacobian Regularization Spatiotemporal Forecasting
Institute of Science and Technology Austria Klosterneuburg, Austria
Exactness Matters for Physical Rule Enforcement
May 8, 2026Bum Jun KimTime Series Forecasting Spatiotemporal Forecasting
The University of Tokyo, Japan
FlightSense: An End-to-End MLOps Platform for Real-Time Flight Delay Prediction via Rotation-Chain Propagation Features and Agentic Conversational AI
May 8, 2026Aditi J. Shelke, Renuka J. Shelke, Yash M. Kamerkar +1Spatiotemporal Forecasting
Stevens Institute of Technology Hoboken, NJ, USA · Axtria Inc. Berkeley Heights, NJ, USA · Meta Menlo Park, CA, USA
Efficient Prompt Learning for Traffic Forecasting
May 8, 2026Qianru Zhang, Xinyi Gao, Alexander Zhou +3OOD Generalization Prompt Tuning
School of Computing and Data Science The University of Hong Kong · School of Electrical Engineering and Computer Science The University of Queensland · Department of Computing The Hong Kong Polytechnic University
TraXion: Rethinking Pre-training Frameworks for Mobility and Beyond
May 7, 2026Shang-Ling Hsu, Mark Tenzer, Cyrus Shahabi +1Self-Supervised Pre-Training Spatiotemporal Forecasting
University of Southern California · Novateur Research Solutions
SpatialEpiBench: Benchmarking Spatial Information and Epidemic Priors in Forecasting
May 7, 2026Ruiqi Lyu, Alistair Turcan, Bryan WilderEpidemic Forecasting Forecasting Benchmarks
1Carnegie Mellon University
SWIFT: Spatio-temporal Wavelet Integrated Forecasting Framework for Workload Traces
May 7, 2026Zeyuan Ding, Lingfeng Zheng, Dian Ding +1Multivariate Time Series Forecasting Time Series Forecasting
1Shanghai Jiao Tong University, Shanghai, China · 2Shaanxi Normal University, Xi’an, China
AirQualityBench: A Realistic Evaluation Benchmark for Global Air Quality Forecasting
May 7, 2026Xing Xu, Xu Wang, Yudong Zhang +3Benchmark Design Time Series Forecasting
University of Science and Technology of China (USTC), Hefei, Anhui, China · 2Suzhou Institute for Advanced Research, USTC, Suzhou, Jiangsu, China · 3The Hong Kong Polytechnic University, Hong Kong, China
CoMemNet: Contrastive Sampling with Memory Replay Network for Continual Traffic Prediction
May 7, 2026Mei Wu, Wenchao Weng, Wenxin Su +2Contrastive Learning Traffic Flow Estimation
College of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China · College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China · School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
Towards Scalable One-Step Generative Modeling for Autoregressive Dynamical System Forecasting
May 7, 2026Tianyue Yang, Xiao XuePDE Surrogate Modeling One-Step Generative Modeling
The Center for Computational Science University College London
Federated Learning for Early Prediction of EV Charging Demand
May 6, 2026Vasilis Perifanis, Foteini Nikolaidou, Nikolaos Pavlidis +2Electric Vehicle Charging Spatiotemporal Forecasting
Indigma Innovations
Delving into Non-Exchangeability for Conformal Prediction in Graph-Structured Multivariate Time Series
May 6, 2026Ruichao Guo, Xingyao Han, Luo Wenshui +3Multivariate Time Series Forecasting Conformal Prediction
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China
Road Risk Monitor: A Deployable U.S. Road Incident Forecasting System with Live Weather and Road-Level Tiles
May 5, 2026Anton IvchenkoIntelligent Transportation Systems Traffic Accident Anticipation
Densification and forecasting of Sentinel-2 time series from multimodal SAR and Optical satellite data using deep generative models
May 5, 2026Véronique Defonte, Dawa Derksen, Alexandre Constantin +1Irregular Time-Series Modeling Remote Sensing
Thales Services Num´eriques 31670 Lab`ege, France · Centre National d’Etudes Spatiales 31400 Toulouse, France
Enabling Real-Time Training of a Wildfire-to-Smoke Map with Multilinear Operators
May 5, 2026Zachary Morrow, Joseph Crockett, John D. Jakeman +1Surrogate Modeling Wildfire Forecasting
Sandia National Laboratories 1515 Eubank Blvd SE, Albuquerque, NM 87123
Conditional Neural Field based Reduced Order Model for Dynamic Ditching Load Prediction
May 5, 2026Henning Schwarz, Pyei Phyo Lin, Jens-Peter M. Zemke +1Neural Surrogate Modeling Implicit Neural Representations
Institute for Fluid Dynamics and Ship Theory, Hamburg University of Technology, Am Schwarzenberg-Campus 4, D-21073 Hamburg, Germany · Institute of Mathematics, Hamburg University of Technology, Am Schwarzenberg-Campus 3, D-21073 Hamburg, Germany
GraphSVR: A Graph Convolutional Support Vector Regression Framework for Robust Spatiotemporal Air Pollution Forecasting
May 5, 2026Nourin Jahan, Muhammed Navas T, Tanujit Chakraborty +1Air Quality Prediction Support Vector Machines
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, India · SAFIR, Sorbonne University, Abu Dhabi, UAE · SCAI - Sorbonne Cluster for Artificial Intelligence, Paris, France +1
Local Truncation Error-Guided Neural ODEs for Large Scale Traffic Forecasting
May 5, 2026Xiao Zhang, Yafei Li, Ruixiang Wang +3Spatiotemporal Forecasting Neural ODEs
Zhengzhou University · Northwestern Polytechnical University
Boundary-Aware Uncertainty Quantification for Wildfire Spread Prediction
May 4, 2026Jonas V. FunkUncertainty Quantification Wildfire Forecasting
Independent Research 76199 Karlsruhe, Germany
CNN-based Multi-In-Multi-Out Model for Efficient Spatiotemporal Prediction
May 2, 2026Hyeonseok JinEfficient Neural Network Inference Precipitation Forecasting
Department of Artificial Intelligence Convergence, Graduate School, Chonnam National University
Arbitrarily Conditioned Hierarchical Flows for Spatiotemporal Events
May 2, 2026Keyan Chen, Qiwei Yuan, Zhitong Xu +2Temporal Point Processes Flow Matching
Kahlert School of Computing University of Utah · Celonis AI
Federated Weather Modeling on Sensor Data
May 1, 2026Shengchao Chen, Guodong LongSpatiotemporal Forecasting Weather Forecasting
Australian AI Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia
Predictive Spatio-Temporal Scene Graphs for Semi-Static Scenes
Apr 30, 2026Miguel Saavedra-Ruiz, Charlie Gauthier, Kumaraditya Gupta +4Spatiotemporal Reasoning Spatiotemporal Forecasting
Department of Computer Science and Operations Research, Universit´e de Montr´eal, Montr´eal, QC, Canada. · Mila - Quebec AI Institute, Montreal, QC, Canada. · Independent Researcher. +1
Comparative Analysis of Polygon-Based and Global Machine Learning Models for Bus Occupancy Prediction
Apr 30, 2026Daniel Azenkot, Michael Fire, Eran Ben EliaMultivariate Time Series Forecasting Time Series Forecasting
Faculty of Computer and Information Science, University of Ben Gurion, Be’er Sheva, Israel · Department of Geography and Environmental Development, Ben-Gurion, Israel
Toward Scalable SDN for LEO Mega-Constellations: A Graph Learning Approach
Apr 30, 2026Sivaram Krishnan, Bassel Al Homssi, Zhouyou Gu +3Graph Neural Networks Wireless Communications
School of Electrical and Mechanical Engineering, University of Adelaide, Adelaide 5000, Australia
PINN-Cast: Exploring the Role of Continuous-Depth NODE in Transformers and Physics Informed Loss as Soft Physical Constraints in Short-term Weather Forecasting
Apr 30, 2026Hira Saleem, Flora Salim, Cormac PurcellTransformer Physics-Informed ML
University of New South Wales, Sydney, NSW, Australia
Continuous Flood Nowcasting in South Asia: A Multi-Sensor Ensemble Remote Sensing Framework for Flood Extent
Apr 29, 2026Usman Nazir, Disha Gomathinayagam, Muhammad Kamran +1Flood Inundation Mapping Remote Sensing
Planetary Health Informatics (PHI) Lab, University of Oxford · Blavatnik School of Government, University of Oxford · PMIU Secretariat, Irrigation Department, Pakistan

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PACT: Peak-Aware Cross-Attention Graph Transformers for Efficient Storm-Surge Emulation

Zesheng Liu, Doyup Kwon, Ning Lin, Maryam Rahnemoonfar

Department of Computer Science and Engineering, Lehigh University · Department of Civil and Environmental Engineering, Princeton University · Department of Civil and Environmental Engineering, Lehigh University

PDE Surrogate Modeling Spatiotemporal Forecasting Time Series Transformers Spatiotemporal GNNs Weather Forecasting

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Accurate and efficient storm-surge emulation is essential for coastal hazard assessment, yet high-fidelity hydrodynamic models remain too expensive for large scenario ensembles and rapid evaluation under heterogeneous climate forcings. We present PACT, a peak-aware cross-attention graph transformer for efficient station-level storm-surge prediction from atmospheric forcing fields. PACT represents each forcing patch as a graph, encodes spatial structure with GraphSAGE, and uses a learned station query to aggregate node information through cross-attention rather than uniform pooling. A Transformer encoder models temporal dependence across the forcing history, and a horizon-query decoder generates lead-specific forecasts from a shared temporal memory. To better capture extreme events, we introduce a peak-aware learning strategy that couples a lightweight auxiliary peak-aware head with a tailored training objective, including a tail-focused loss on peak-dominated samples and a horizon-wise slope regularizer to encourage coherent multi-step evolution. Across multiple tide-gauge stations along the US Northeast coast, PACT outperforms a strong spatio-temporal graph neural network baseline in both RMSE and MAE. Diagnostics show improved peak fidelity and tail preservation for reanalysis and most CMIP6 datasets. PACT is also computationally efficient, requiring about 3.5~s to generate a full winter-season surge trajectory for one year after training. Under distribution shift across five CMIP6 forcings, PACT transfers well within the CMIP6 family but degrades markedly when transferring from reanalysis to climate-model forcings, highlighting a persistent reanalysis--GCM gap.

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