Probabilistic Forecasting

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Probabilistic forecasting estimates ranges, quantiles, event probabilities, or full predictive distributions rather than only a single future value. Methods include Bayesian and parametric models, ensemble post-processing and conformal calibration, and neural generative approaches such as diffusion and flow matching; language-model agents also forecast real-world events using evidence and probability aggregation. Researchers evaluate forecasts with proper scores such as CRPS and Brier score, alongside calibration, sharpness, and benchmarks in weather, energy, health, finance, and human mobility. A central challenge is balancing accuracy, reliable uncertainty, computational cost, and usefulness for downstream decisions, since strong average scores may hide failures in extremes or dependencies between outcomes.

Latest papers

230 papers37 papers in the last four weeks, up 3% on the four weeks before. 0.2% of all new papers.

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
MUSCLE-NET: Predicted-Multiscale-Aware Network for Pedestrian Trajectory Forecasting
May 30, 2026Yu Liu, Ming Huang, Xiao Ren +3Pedestrian Behavior Modeling Pedestrian Trajectory Prediction
Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, School of Automation and Intelligent Manufacturing, Southern University of Science and Technology (SUSTech), Shenzhen 518055, China · Department of Mechanical Engineering, City University of Hong Kong, Hong Kong SAR, China
Probabilistic Precipitation Nowcasting with Rectified Flow Transformers
May 29, 2026Johannes Schusterbauer, Jannik Wiese, Nick Stracke +2Precipitation Forecasting Rectified Flow
ForecastCompass: Guiding Agentic Forecasting with Adaptive Factor Memory
May 29, 2026Yurui Chang, Yongkang Du, Yuanpu Cao +2Probabilistic Forecasting Memory-Augmented Agents
Pennsylvania State University
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
Probabilistic bias adjustment of seasonal forecasts using generative machine learning: A case study of Arctic sea ice predictions
May 27, 2026Parsa Gooya, Reinel Sospedra-AlfonsoTime Series Forecasting Probabilistic Forecasting
Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada
Stabilizing distribution-free probabilistic forecasts
May 27, 2026Jente Van Belle, Honglin Wen, Wouter Verbeke +1Probabilistic Forecasting Probabilistic Time Series Forecasting
Faculty of Economics and Business, KU Leuven, Belgium · Department of Electrical Engineering, Shanghai Jiao Tong University, China · Dyson School of Design Engineering, Imperial College London, United Kingdom +3
Aligning LLMs with Human Uncertainty: A Beta-Bernoulli Calibrator for LLM Forecasting
May 26, 2026Hui Dai, Ryan Teehan, Parsa Torabian +1Post-Hoc Calibration Probabilistic Forecasting
Agentic Learning AI Lab, New York University · Chronologies AI
Agent-Centric Social Trajectory Prediction: A Free Energy Principle Perspective
May 25, 2026Yanping Wu, Ji Zhang, Hao Chen +2Multi-Agent Trajectory Prediction Probabilistic Forecasting
University of Glasgow Glasgow, UK · Southwest Jiaotong University Chengdu, China
Deep Learning-Enabled Prediction of Geoeffective CMEs Using SOHO and SDO Observations
May 23, 2026Zhaoxin Yan, Jason T. L. Wang, Haimin Wang +5Probabilistic Forecasting
Institute for Space Weather Sciences, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA · Department of Computer Science, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA · Center for Solar-Terrestrial Research, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA +1
Graph-based Complexity Forecasts in UK En Route Airspace Using Relevant Aircraft Interactions
May 22, 2026Edward Henderson, George De Ath, Nick PepperAir Traffic Management Intelligent Transportation Systems
The Alan Turing Institute · University of Exeter
Is Capability a Liability? More Capable Language Models Make Worse Forecasts When It Matters Most
May 21, 2026Nick Merrill, Jaeho Lee, Ezra KargerLanguage Model Scaling Laws Forecasting Benchmarks
Forecasting Research Institute · UC Berkeley
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
A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting
May 21, 2026Yinsong Chen, Samson S. Yu, Kashem M. MuttaqiUncertainty Quantification Time Series Forecasting
School of Engineering, Deakin University, Melbourne, VIC 3216, Australia · ARC Training Centre in Energy Technologies for Future Grids, School of Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
Evaluating Neural Probabilistic Forecasts of Sparse Earthquake Counts: What Survives a Careful Evaluation
May 20, 2026Alim IgilikProbabilistic Forecasting Seismology
[physics.geo-ph]
Deep Learning Surrogates for Emulating Stochastic Climate Tipping Dynamics
May 20, 2026Adeline Hillier, Jennifer Sleeman, Jay Brett +3Time Series Transformers Multivariate Time Series Forecasting
Johns Hopkins Applied Physics Laboratory Hopkins University · Johns
Improving Ensemble CAPE Forecasts with a Diffusion Model Incorporating Aerosol Information
May 19, 2026Zachary James, Joseph Guinness, Arthur DeGaetanoProbabilistic Forecasting Diffusion Models
Cornell University, Department of Statistics and Data Science · Washington University in St. Louis, Department of Statistics and Data Science · Cornell University, Department of Earth and Atmospheric Sciences
Probabilistic Multivariate Time Series Forecasting with Diffusion Copulas
May 19, 2026David Huk, Dongshan Wang, Miha BresarMultivariate Time Series Forecasting Copula Models
Department of Statistics The University of Warwick · School of Data Science The Chinese University of Hong Kong, Shenzhen
Accurate, Efficient, and Explainable Deep Learning Approaches for Environmental Science Problems
May 19, 2026Jimeng ShiRetrieval-Augmented Generation Explainable Artificial Intelligence
FLORIDA INTERNATIONAL UNIVERSITY Miami, Florida
Bayesian-Monte Carlo Schedule Updating for Construction Digital Twins: A Probabilistic Framework for Dynamic Project Forecasting
May 17, 2026Atena Khoshkonesh, Mohsen Mohammadagha, Vinayak Kaushal +1Probabilistic Forecasting Digital Twins
1* M.S. Graduate, Department of Civil Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA. · 2Ph.D. Researcher, Department of Civil Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA. · 3Associate Professor of Instruction, Department of Civil Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA.
SwAIther-Precip: Lead-Time-Aware Bias Correction Enables Kilometer-Scale Downscaling of Global AI Precipitation Forecasts over Switzerland
May 15, 2026Dan Assouline, Erwan Koch, Federico Amato +5Precipitation Forecasting Time Series Forecasting
Swiss Data Science Center, ETH, Zürich, Switzerland · Expertise Center for Climate Extremes, University of Lausanne, Lausanne, Switzerland · Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland +1
Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting
May 14, 2026Daniel Holmberg, Joel Oskarsson, Erik Wikingsson +2Graph Neural Networks Uncertainty Quantification
University of Helsinki · ETH AI Center · Linköping University
VMU-Diff: A Coarse-to-fine Multi-source Data Fusion Framework for Precipitation Nowcasting
May 14, 2026Chunlei Shi, Hao Li, Yufeng Zhu +6Precipitation Forecasting Probabilistic Forecasting
Department of Automation, Southeast University, Nanjing 210096, China
When Individually Calibrated Models Become Collectively Miscalibrated
May 14, 2026Zhaohui WangProbability Calibration Game Theory
USC Viterbi School of Engineering, University of Southern California
LEAF: A Living Benchmark for Event-Augmented Forecasting
May 9, 2026Mingtian Tan, Mihir Parmar, Palash Goyal +5Financial Forecasting Time Series Forecasting
Google · University of Virginia
Tyche: One Step Flow for Efficient Probabilistic Weather Forecasting
May 7, 2026Fan Xu, Yuan Gao, Kun Wang +4Flow Matching Probabilistic Forecasting
1USTC · 2SLAI · 3THU +3
Two Hebrew folk meteorological proverbs tested: rainfall on Rosh Chodesh and Shabbat Mevarechim as predictors of monthly precipitation (Israel, 1950-2024)
May 6, 2026Abraham Itzhak WeinbergPrecipitation Forecasting Probabilistic Forecasting
AI-WEINBERG, AI Experts [physics.ao-ph]
Towards accurate extreme event likelihoods from diffusion model climate emulators
May 5, 2026Peter Manshausen, Noah Brenowitz, Julius Berner +2Diffusion Model Guidance Climate Science
NVIDIA, Santa Clara, CA, USA
Foresight Arena: An On-Chain Benchmark for Evaluating AI Forecasting Agents
May 1, 2026Maksym Nechepurenko, Pavel ShuvalovAI Agent Evaluation Forecasting Benchmarks
Director of Research, Devnull, Dubai, UAE · Chief Technology Officer, Devnull, Dubai, UAE

May 30, 2026J/K move · Enter open · S save

EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction

Dahai Yu, Rongchao Xu, Lin Jiang, Guang Wang

Florida State University · Tallahassee, Florida, USA

Selective SSMs Time Series Forecasting Probabilistic Forecasting Conformal Prediction Spatiotemporal Forecasting Electricity Load Forecasting+1

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Energy consumption prediction is essential for efficient grid management, demand-side optimization, and sustainable energy planning. Although advanced machine learning methods have been employed for better prediction performance, existing works have two key limitations: (1) they usually formulate this task as a purely time-series prediction problem without explicitly modeling the spatial dependencies among different regions, and (2) they fail to provide reliable predictions with uncertainty estimates under abnormal situations such as extreme weather events. To advance existing research, we propose EnergyMamba, an uncertainty-aware spatiotemporal learning framework for accurate and reliable energy consumption prediction, which comprises two key components: (i) a novel Graph-Enhanced Selective State Space Model (GE-Mamba) that injects spatial context learned from the grid topology into the temporal dynamics, enabling coupled spatiotemporal modeling, and (ii) an Adaptive Sequential Conformalized Quantile Regression (AS-CQR) module, which includes locally adaptive normalization and an online feedback mechanism to dynamically calibrate prediction intervals under potential distribution shifts. We evaluate EnergyMamba on four large-scale real-world datasets from Florida, New York, and California. Results show EnergyMamba achieves around 5% improvement in prediction accuracy and 6% improvement in uncertainty quantification over 15 state-of-the-art baselines.