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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CRPS-LAM: Probabilistic Regional Weather Forecasting with Continuous Ranked Probability Score
Oct 10, 2025Erik Larsson, Joel Oskarsson, Tomas Landelius +1Graph Neural Networks Ensemble Learning
Linköping University · ETH AI Center · SMHI & Linköping University
Double-Diffusion: Balancing Speed, Accuracy, and Uncertainty in Probabilistic Forecasting for Urban Sensor Networks
Jun 29, 2025Hanlin Dong, Arian Prabowo, Hao Xue +4Spatiotemporal Forecasting Diffusion Model Inference Acceleration
University of New South Wales Sydney, Australia · University of Maryland College Park, United States · The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, China
CanadaFireSat: Toward high-resolution wildfire forecasting with multiple modalities
Jun 10, 2025Hugo Porta, Emanuele Dalsasso, Jessica L. McCarty +1Wildfire Forecasting Forecasting Benchmarks
EPFL, Route des Ronquos 86, Sion, 1950, Wallis, Switzerland · Université Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, 38000, France · NASA Ames Research Center, Earth Science Division„ Moffett Field, California, 94035, USA
Uncovering Insights of Compound Flooding with Data-Driven AI
Jun 4, 2025Xu Zheng, Chaohao Lin, Sipeng Chen +7Spatiotemporal Forecasting Flood Forecasting
Florida International University Miami, FL, USA · Florida State University Tallahassee, FL, USA · UIUC Champaign, IL, USA +3
DRAN: A Distribution and Relation Adaptive Network for Spatio-temporal Forecasting
Apr 2, 2025Xiaobei Zou, Luolin Xiong, Kexuan Zhang +2Non-Stationary Time Series Forecasting Dynamic Graph Learning
Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China · Faculty of Informatics, Università della Svizzera italiana, 69000 Lugano, Switzerland · Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
Spatio-Temporal Partial Sensing Forecast for Long-term Traffic
Aug 2, 2024Zibo Liu, Zhe Jiang, Zelin Xu +5Long-Term Time Series Forecasting Spatiotemporal Forecasting
Department of Computer & Information Science & Engineering, University of Florida · Department of Computer Science, University of Kentucky
Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks
Apr 30, 2024Yi Li, Renyou Xie, Chaojie Li +2Byzantine-Robust Federated Learning Electric Vehicle Charging
College of Electronic and Information Engineering, Southwest University · Department of Electrical Engineering, City University of Hong Kong, Hong Kong · Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong
Learning Intrinsic Water-Quality Dynamics with Rainfall for Data-Driven Forecasting
Date pendingZiqi Wang, Hailiang Zhao, Cheng Bao +3Multivariate Time Series Forecasting Time Series Forecasting
Zhejiang University
Using Seismic Statistical Features and VQ-VAE to Improve Spatiotemporal Seismicity Predictability
Date pendingWei Quan, Denise GorseVector-Quantized Variational Autoencoder Seismology
Department of Computer Science University College London

Oct 10, 2025, cs.LGJ/K move · Enter open · S save

CRPS-LAM: Probabilistic Regional Weather Forecasting with Continuous Ranked Probability Score

Erik Larsson, Joel Oskarsson, Tomas Landelius, Fredrik Lindsten

Linköping University · ETH AI Center · SMHI & Linköping University

Graph Neural Networks Ensemble Learning Probabilistic Forecasting Spatiotemporal Forecasting Weather Forecasting

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Limited-Area Models (LAMs) enable weather forecasting over regional domains at higher resolutions than what is computationally feasible for global models. At such high resolutions, machine learning approaches for weather prediction increasingly rely on ensemble methods to produce probabilistic forecasts. However, existing machine learning LAMs are not scalable due to relying on computationally costly diffusion models or inefficient graph neural networks. We tackle this by introducing a new hybrid CNN/GNN architecture, tailored to the LAM weather forecasting problem. Using this architecture, we construct the DET-LAM deterministic model, producing LAM forecasts both more efficiently and accurately than its graph-based competitor. We then tackle the ensemble forecasting problem, by using this architecture as a backbone for the generative model CRPS-LAM. CRPS-LAM is trained using a Continuous Ranked Probability Score (CRPS) objective, enabling efficient training and sampling in a single forward pass. This yields a speedup of

\approx \times 39

compared to diffusion-based baselines. We evaluate our approach on regional domains in northern Europe, demonstrating that CRPS-LAM produces skillful and well-calibrated forecasts across a range of atmospheric variables.

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