Spatiotemporal Forecasting

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Spatiotemporal forecasting studies how patterns evolve across both space and time, supporting decisions such as routing traffic, preparing for floods, and tracking disease outbreaks. Common approaches include graph neural networks for connected locations, convolutional and Transformer models for gridded fields, and state-space or neural-operator methods for long sequences and physical dynamics. Researchers also use physics-informed constraints, multimodal data, and probabilistic or generative models to represent mechanisms and forecast uncertainty. A central challenge is making forecasts stable and calibrated over long horizons while generalizing to sparse observations, new regions, and changing conditions; realistic benchmarks often reveal weaknesses hidden by standard test splits.

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429 papers59 papers in the last four weeks, up 48% on the four weeks before. 0.3% of all new papers.

Learning Intrinsic Water-Quality Dynamics with Rainfall for Data-Driven Forecasting
Aug 1, 2025Ziqi Wang, Hailiang Zhao, Cheng Bao +3Multivariate Time Series Forecasting Time Series Forecasting
Zhejiang University
NeuralDMD: Interpretable Neural Representation of Dynamics from Sparse and Noisy Measurements
Jul 3, 2025Ali SaraerToosi, Renbo Tu, Esther Y. H. Lin +2Spatiotemporal Forecasting Inverse Problems
University of Toronto, Toronto, ON, Canada · Vector Institute, Toronto, ON, Canada · NVIDIA Corporation, USA
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
Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models
Jul 28, 2024Jifeng Wang, Kaouther Messaoud, Yuejiang Liu +2Parameter-Efficient Fine-Tuning Adapter Tuning
University of Bonn, Bonn, Germany · VITA Laboratory, EPFL, Lausanne, Switzerland · Stanford University, Stanford, USA
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

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Learning Intrinsic Water-Quality Dynamics with Rainfall for Data-Driven Forecasting

Ziqi Wang, Hailiang Zhao, Cheng Bao, Daojiang Hu+2

Zhejiang University

Multivariate Time Series Forecasting Time Series Forecasting Spatiotemporal Forecasting Multiscale Time-Series Modeling

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Rainfall is an important environmental driver of water-quality variations through processes such as runoff, pollutant transport, dilution, and resuspension. Traditional mechanistic models can explicitly describe these processes but often require substantial process specification and site-specific calibration, limiting their flexibility under changing hydrological conditions. In this work, we explore a data-driven alternative by proposing RaiNet to jointly model multiscale water-quality dynamics and station-specific rainfall effects across relative lags and temporal scales. RaiNet employs LocTrend to capture irregular water-quality dynamics, constructs station-oriented rainfall events from gridded precipitation, and introduces XGateFusion for conditional lag-aware fusion across scales. We further release three real-world multimodal datasets comprising over 150,000 temporally aligned water quality observations and gridded precipitation raster images. Experiments show that RaiNet outperforms general time-series, water quality, diffusion-based, and spatiotemporal models by over 20%, while component-wise analyses confirm the distinct contribution of each module.