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.

Proper Scoring Rule-based Diffusion for Probabilistic Weather Forecasting
Sep 29, 2026Joonhyeong Park, Giung Nam, Hyungi Lee +3Probabilistic Forecasting Proper Scoring Rules
KAIST · Kookmin University · New York University
NowcastDiT: Diffusion Transformers are Effective Precipitation Nowcasters
Sep 29, 2026Haoran Xu, Xingzhuo Guo, Yuchen Zhang +3Precipitation Forecasting Diffusion Transformer
School of Software, BNRist, Tsinghua University, China
CHAIN: Calibrated LLM Forecasting via Causal-Temporal Hypergraph Inference
Sep 29, 2026Wenjin Liu, Chenxi Wang, Yue Lu +2Probabilistic Forecasting Language Model Calibration
Hithink research · Nanyang Technological University
Predictive Credit: Measuring What Scientific Explanations Add to Experimental Forecasts
Sep 29, 2026Jingjie Ning, Xueqi Li, Yibo Kong +1Forecasting Benchmarks Probabilistic Forecasting
Carnegie Mellon University · Tsinghua University
A Hierarchy of Entropy-Shapley Games for Multivariate Predictive Uncertainty
Sep 28, 2026Niklas Koenen, Claudia Battistin, Jeriek Van den Abeele +1Probabilistic Forecasting Shapley Value Attribution
Leibniz Institute for Prevention Research and Epidemiology – BIPS University of Bremen, Germany · Simula Research Laboratory Oslo, Norway · Telenor Research & Innovation Fornebu, Norway +1
MW-Nowcast: Six-hour ensemble nowcasting of extreme precipitation
Sep 28, 2026Ning Wang, Zuliang Fang, Weixin Jin +19Precipitation Forecasting Probabilistic Forecasting
Microsoft Corporation. · City University of Hong Kong. · University of Cambridge.
Probabilistic electrical power demand forecasting with uncertainty quantification
Sep 28, 2026Mahesh Neupane, Pragya Dhungana, Pradip Khatri +2Uncertainty Quantification Probabilistic Forecasting
Interconnection and Numbering Section Nepal Telecommunications Authority Kathmandu, Nepal · Nepal Electricity Authority Kathmandu, Nepal · Department of Global Public Health and Primary Care University of Bergen Bergen, Norway +1
SLP-ProbHard: Probabilistic Hard-Constrained Learning via Structural Latent Parameterization
Sep 27, 2026Wondesen Teshome Bekele, Marco D'OriaConstrained Generative Modeling Latent Variable Models
Department of Engineering and Architecture, University of Parma, Parma, Italy · University School for Advanced Studies IUSS Pavia, Pavia, Italy
Two Heads Are Better Than One: Aggregating Weaker LLMs for Better Forecasts
Sep 27, 2026Cheng Peng, Ruixi Luo, Zhi Chen +1Language Model Ensembles Probabilistic Forecasting
Chinese University of Hong Kong
BridgeCast: Bridging Ocean Wave Forecasts to Reanalysis via Flow Matching with Exogenous Variables
Sep 27, 2026Siyu Gan, Dongsheng Luo, Kunxiaojia Yuan +2Spatiotemporal Forecasting Time Series Forecasting
Johns Hopkins University · Singapore Management University · University of Houston +1
Can LLMs Predict the Future? A Brier Score Analysis of Prediction Markets
Sep 26, 2026Yuanbo Li, Zekun Li, Xiaoyan congProbabilistic Forecasting LLM Evaluation
Brown University Providence, RI 02912, USA
UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting
Sep 25, 2026Derrick Gilchrist Edward Manoharan, Eljas Linna, Kestutis Baltakys +2Limit Order Book Forecasting Uncertainty Quantification
Data Science Research Centre Tampere University Tampere, Finland
Electric Potential Patterns Forecasting in the Southern Hemisphere with Deep Learning Techniques
Sep 25, 2026Francesco Pio Ramunno, Simone Mestici, Igino Coco +5Spatiotemporal Forecasting Probabilistic Forecasting
University of Applied Sciences and Arts Nortwestern Switzerland · Department of Computer Science, University of Geneva, 1211 Geneva, Switzerland · Universit´a Roma ”La Sapienza” +3
Suitable Measures for the Potential Operational Utility of AI NWP Rainfall Forecasts Over Africa
Sep 24, 2026Shruti Nath, Docko Sow, Koomi Toussaint Amoussouvi +5Weather Forecasting Precipitation Forecasting
Department of Physics, University of Oxford, United Kingdom · AfriClimate AI, South Africa · ECMWF, Reading, United Kingdom +1
Lightweight Probabilistic Downscaling from a Deterministic Base Model
Sep 24, 2026Joseph McLean, Tiffany Vlaar, Sigrid Passano Hellan +1Climate Science U-Net
University of Glasgow · Climate System and Climate Services, NORCE Research AS and Bjerknes Centre for Climate Research, Bergen, Norway
Kairos: Grounded Forecasting of Presence and Directional Flow in 4D Scene Graphs
Sep 23, 2026Iacopo Catalano, Julio A. Placed, Javier Civera +1Spatiotemporal Forecasting Probabilistic Forecasting
University of Turku, Finland · Instituto Tecnol´ogico de Arag´on (ITA) and the University of Zaragoza, Spain · University of Zaragoza, Spain +1
Interweaving Marginals into Multivariate Sample Paths: Training-Free Dependence Construction for Probabilistic Time Series Foundation Models
Sep 22, 2026Jinmyeong Choi, Jinkwan Jang, Seul Lee +1Probabilistic Forecasting Time-Series Foundation Models
Graduate School of Data Science Seoul National University
FAST-ML: A Hybrid Physics-Machine Learning Framework for Tropical Cyclone Intensity Forecasting
Sep 22, 2026Shijie Xiao, Jonathan Lin, Thomas Ehrmann +1Probabilistic Forecasting Physics-Informed ML
School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA · School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, USA · Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, USA +1
The Operational Value of Spatial Dependence in Renewable Forecast Scenarios for Single-Period Economic Dispatch: A Controlled Ablation Study
Sep 21, 2026Jayakumar ManoharanTime Series Forecasting Probabilistic Forecasting
Electric Power Research Institute (EPRI), Charlotte, NC, USA
Probabilistic Forecasting of Business Process Executions with Neural Temporal Point Processes
Sep 18, 2026Jiaxin Yuan, Daniela Grigori, Han van der AaProbabilistic Forecasting Predictive Process Monitoring
Paris Dauphine University-PSL, Pl. du Maréchal de Lattre de Tassigny, 75016 Paris, France · University of Vienna, Universitätsring 1, 1010 Wien, Austria
Butterfly Effect and the Kinetic Energy Cascade in Probabilistic Machine Learning Weather Prediction Models
Sep 16, 2026Jiakai Chen, Joel Oskarsson, Simon Driscoll +1Probabilistic Forecasting
Department of Physics, University of Cambridge, Cambridge, UK · ETH AI Center, ETH Zurich, Zurich, Switzerland · Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
Efficient Analog-Initialized Latent Transport for Spatially Coherent Probabilistic Downscaling from Global to Kilometer Scales
Sep 15, 2026Ophélia MirallesProbabilistic Forecasting Weather Forecasting
Norwegian Meteorological Institute, Oslo, Norway
Goal-oriented probabilistic forecasting for dynamic PRB allocation in 5G networks
Sep 15, 2026Oier Larumbe-Lizarraga, Roberto Pereira, Cristian J. Vaca-RubioProbabilistic Forecasting Wireless Resource Allocation
Universidad Isabel I, Burgos, Spain · Keysight Technologies, Barcelona, Spain · Ericsson Research, Stockholm, Sweden
IRENE: A Convolutional GRU Ensemble Model for Radar Precipitation Nowcasting over Italy
Sep 15, 2026Alessandro Camilletti, Gabriele Franch, Elena Tomasi +1Convolutional RNNs Precipitation Forecasting
Fondazione Bruno Kessler
When Should a World Model Move? Loss-Conditioned State Execution
Sep 14, 2026Jintao Xu, Zhengyu Chen, Ben Zhang +2Selective Prediction Probabilistic Forecasting
Supply Chain Tech Team Y, JD.com
MoveBench: A Benchmark for Global-Scale Wildlife Movement Forecasting
Sep 14, 2026Justin Kay, Shir Bar, Ellen O. Aikens +28Forecasting Benchmarks Probabilistic Forecasting
MIT · University of Wyoming · Marburg University, Hessian.AI +16
Beyond Point Forecasts: A Survey on Probabilistic Forecasting for Time Series and Spatiotemporal Data
Sep 11, 2026Donia Besher, Rajdeep Pathak, Madhurima Panja +1Probabilistic Forecasting Probabilistic Time Series Forecasting
SAFIR, Sorbonne University Abu Dhabi, United Arab Emirates · SCAI, Sorbonne Université, Paris, France
4D Parallelism Unlocks Exascale Bayesian Neural Networks for High-Fidelity Atmospheric Modeling
Sep 11, 2026Deifilia Kieckhefen, Juan Pedro Gutiérrez Hermosillo Muriedas, Lars Helge Heyen +12Bayesian Neural Networks Uncertainty Quantification
Karlsruhe Institute of Technology, Germany · Forschungszentrum Jülich, Germany · CSC – IT Center for Science Ltd., Finnland +1
Information Specialization and Constrained Synthesis in Multi-Agent LLM Forecasting: A Prospective Live-Study of the 2026 FIFA World Cup
Sep 11, 2026Julian Varghese, Lucas Bickmann, Sarah SandmannProbabilistic Forecasting Multi-Agent Collaboration
Institute of Medical Informatics, Otto-von-Guericke University Magdeburg, Germany.
When Do Language Models Add Value? Relative Competence in Hybrid Event Forecasting
Sep 10, 2026Aditi Tiwari, Aashrith Bandaru, Heng JiEvent Time Prediction Selective Prediction
University of Illinois Urbana-Champaign Urbana, Illinois, USA

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Proper Scoring Rule-based Diffusion for Probabilistic Weather Forecasting

Joonhyeong Park, Giung Nam, Hyungi Lee, Kyunghyun Cho+2

KAIST · Kookmin University · New York University

Probabilistic Forecasting Proper Scoring Rules Conditional Diffusion Models Weather Forecasting

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Recent probabilistic weather forecasters train stochastic predictors with the continuous ranked probability score (CRPS) to generate each ensemble member in a single forward pass. These models learn the predictive distribution from the forecast context alone, which becomes difficult at longer forecast horizons where uncertainty is high. To learn the predictive distribution more effectively, we introduce auxiliary conditional denoising tasks that predict the same future state from the context and its corrupted version, which provides partial future information that can reduce prediction ambiguity. Building on distributional diffusion models, we learn the conditional distributions of these tasks with a single stochastic predictor by minimizing a proper scoring rule across noise levels. At inference, the predictor can still generate each ensemble member in a single forward pass at the fully corrupted endpoint. Standard CRPS training is recovered as the endpoint-only special case of our formulation, so our framework extends existing CRPS-based forecasters with only additional conditioning inputs. Controlled experiments show that the auxiliary tasks improve one-step forecasting across architectures, with larger gains at longer forecast horizons. The gains extend to high-dimensional global weather forecasting under both training from scratch and fine-tuning, along with improved calibration and potential benefits for generalization under distribution shift.