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.

When to Trust Memory: Retrieval-Guided Probabilistic Spatiotemporal Forecasting under Distribution Shift
Oct 23, 2026Haochen Lv, Jianhao Zhang, Zhichen Lei +5Spatiotemporal Forecasting Probabilistic Forecasting
School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China. · Key Laboratory of Big Data and Artificial Intelligence in Transportation, Ministry of Education, Beijing, China. · Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing, China.
Observation Assimilation Transformer: Probabilistic Weather Analysis from Sparse Station Observations
Oct 20, 2026Christian Donner, Michele Cattaneo, Maxim Samarin +4Data Assimilation Probabilistic Forecasting
Swiss Data Science Center, Eidgenössische Technische Hochschule Zürich (ETHZ) · Federal Office of Meteorology and Climatology MeteoSwiss, Switzerland
Wet-Biased Data Pipelines Mislead the Training and Evaluation of Generative Precipitation Nowcasting
Oct 19, 2026Christoph Metzl, Stefan Kapsreiter, Kianusch Vahid Yousefnia +4Precipitation Nowcasting Generative AI Evaluation
Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany · Wismar University of Applied Sciences, Wismar, Germany · Arpae Emilia-Romagna, Hydro-Meteo-Climate Service (SIMC), Bologna, Italy +1
Global probabilistic forecasting of air pollution
Oct 19, 2026Ammar Kheder, Michael Boy, Zhi-song LiuAir Quality Prediction Probabilistic Forecasting
Department of Computational Engineering, LUT University, Lahti, Finland · Institute for Atmospheric and Earth System Research, University of Helsinki, Helsinki, Finland · Atmospheric Modelling Centre Lahti (AMC-Lahti), Lahti, Finland
Beyond the record: evaluating probabilistic AI weather forecasts deep in the tail
Oct 18, 2026Bastiaan Van Velthoven, Cas Decancq, Diego G. Miralles +2Weather Forecasting Probabilistic Forecasting
AlphaBet: Reinforcement Learning for LLM Event Forecasting Agents
Oct 15, 2026Liqin Ye, Rongzhi Zhang, Xin Liu +5Probabilistic Forecasting RL for LLM Agents
Georgia Institute of Technology · Amazon
Can AI Forecast the Extremes? Evaluating Probabilistic Weather Models on Tropical Cyclones
Oct 15, 2026Nathan Chalumeau, Xavier Renard, Remi Meynadier +3Tropical Cyclone Intensity Forecasting Weather Forecasting
INRIA, Paris, France · AXA, Paris, France · Sorbonne Université, CNRS, LIP6, F-75005 Paris, France +1
Single-stage joint diffusion of sky images and clear-sky index for ultra-short-term irradiance forecasting: what the frame-to-irradiance stage discards
Oct 15, 2026Kanawut Suwandee, Suwichaya SuwanwimolkulSolar Irradiance Forecasting Time Series Forecasting
Chulalongkorn University, 254 Phaya Thai Rd, Wang Mai, Pathum Wan, Bangkok, 10330, Thailand
DIAL: Dynamic and Informed Adjustment of the Native Stochastic Law for Reliable Weather Ensembles
Oct 15, 2026Yucheng Wang, Haonan Guo, Tianhao Zhao +4Weather Forecasting Probabilistic Forecasting
Wuhan University · Zhongguancun Academy
Online Quantile Omniprediction for Proper Losses
Oct 15, 2026Hyunsuk Kim, Isaac Gibbs, Michael W. Mahoney +1Probabilistic Forecasting Quantile Regression
Department of Statistics, University of California, Berkeley · Department of Statistics, University of Toronto · ICSI, LBNL, and Dept. of Statistics, University of California, Berkeley
Forecast lead time and the timing of crew deployment for weather-driven power outages using AI weather models
Oct 14, 2026Mohammad Kian Golkar, Luciano Alves de Oliveira, Mohamad AlipourDisaster Response Weather Forecasting
Department of Agricultural and Biological Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA · Department of Civil Engineering, The Grainger College of Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61821, USA
Probabilistic Forecasting of Wildfire Size Using Conditional Flow Matching
Oct 11, 2026Isaac Lee, Bryan Shaddy, Assad OberaiWildfire Forecasting Probabilistic Forecasting
Department of Mathematics, University of Southern California · Department of Aerospace and Mechanical Engineering, University of Southern California
OceanFlow: Coherent Flow Matching for Probabilistic Ocean Modeling
Oct 11, 2026Yuan Gao, Fan Xu, Hao Wu +1Probabilistic Forecasting Flow Matching
Tsinghua University · University of Science and Technology of China
Probabilistic Ship Trajectory Forecasting using Transformer Models on AIS Data
Oct 9, 2026Frederik E. T. SchöllerVessel Trajectory Prediction Probabilistic Forecasting
Automation and Control Group, Department of Electrical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark · Technical University of Denmark
Diffusion Precipitation Generation with Medium-range Weather Forecasting
Oct 9, 2026Guowen Li, Yang Liu, Mengxuan Chen +11Precipitation Forecasting Weather Forecasting
Tsinghua University · The Chinese University of Hong Kong · The Hong Kong University of S & T +5
Just Weather Scoring: Efficient End-to-end Nowcasting with Distributional Diffusion
Oct 8, 2026Jannik Wiese, Johannes Schusterbauer, Tommaso Martorella +1Precipitation Nowcasting Probabilistic Forecasting
Seq-Flow: Efficient Probabilistic Forecasting with Self-Rollout Error Control
Oct 7, 2026Yinan Huang, Shitij Govil, Bo Dai +1Probabilistic Forecasting Probabilistic Time Series Forecasting
Georgia Institute of Technology
Towards Calibrated Probabilistic Forecasts for Events of Interest via Outcome-Conditional Recalibration
Oct 7, 2026Jakob Benjamin Wessel, Sam AllenPost-Hoc Calibration Probabilistic Forecasting
Institute for Meteorology University of Leipzig · Institute of Statistics Karlsruhe Institute of Technology
Learning joint probabilistic weather forecasts from station observations alone
Oct 7, 2026Chaeyeon Yi, Yun Am SeoWeather Forecasting Probabilistic Forecasting
Research Center for Atmospheric Environment, Hankuk University of Foreign Studies, Yongin 17035, Republic of Korea · NAVI Hyper-Tropicalization Research Institute, Jeju National University, Jeju 63243, Republic of Korea · Department of Data Science, Jeju National University, Jeju 63243, Republic of Korea
EC-EarthFlow: Probabilistic emulation of daily transient global climate model simulations with flow matching
Oct 7, 2026Kirien Whan, Nikolaj T. Mücke, Karin van der WielClimate Emulation Climate Modeling
KNMI, Utrechtseweg 297, De Bilt, 3731GA, The Netherlands · Scientific Computing, Centrum Wiskunde & Informatica, Science Park 123, Amsterdam, 1098XG, The Netherlands · Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, The Netherlands +1
Explicit Asymptotic Bounds for Sequential Calibration Beyond $T^{2/3}$
Oct 6, 2026Eric Dai, Maxwell FishelsonProbability Calibration Probabilistic Forecasting
Edison Academy Magnet School · Institute for Advanced Study
Xaurora: Generative Weather Forecasting with Denoising Stochastic Interpolants from a Foundation Model Prior
Oct 5, 2026Eliot Walt, Miltiadis Kofinas, Nikolaj Mücke +2Probabilistic Forecasting Stochastic Interpolants
Vrije Universiteit Amsterdam · University of Amsterdam · Delft University of Technology +1
When better traffic forecasts fail to improve signal control: a layered diagnostic study of forecast-to-decision value
Oct 4, 2026Jianing Long, Xiaobin Li, Wuming Lei +1Traffic Signal Control Traffic Forecasting
School of Transportation Engineering East China Jiaotong University
Probabilistic Pedestrian Forecasts from a Handheld Phone: World-Frame Heat Maps, Visual-Inertial Height Drift, and Evaluation without Ground Truth
Oct 3, 2026Danial SafaeiPedestrian Trajectory Prediction Probabilistic Forecasting
WMG, University of Warwick, Coventry, United Kingdom
ClimateBench v2.0: Probabilistic Climate Model Benchmarking
Oct 3, 2026Duncan Watson-Parris, Willa Tobin, Aytaç Paçal +32Climate Modeling Benchmark Design
Scripps Institution of Oceanography, University of California San Diego · Halicioglu Data Science Institute, University of California San Diego · University of Tübingen, connAIx Research School for Applied AI, Heilbronn, Germany +23
SDECast: Probabilistic Weather Forecasting in Continuous Time with Neural SDEs
Oct 2, 2026Maria Marchenko, Martin Andrae, Fredrik Lindsten +1Weather Forecasting Probabilistic Forecasting
Linköping University, Sweden
Scale-Recursive Rectified Flows for Few-Step Precipitation Ensembles
Oct 2, 2026Shunya Nagashima, Takumi BannaiRectified Flow Precipitation Downscaling
Neurogica Inc. · LTS, Inc. · ME-Lab Japan, Inc.
Do Your Own Research: Learning to Forecast by Learning to Search
Oct 1, 2026Yusuf Afifi, Artur Kiulian, Anton Polishko +3Agentic RL Forecasting Benchmarks
Future Principle
Variational Streaming Flow: Probabilistic Forecasting in Physical Time
Oct 1, 2026Hans Hao-Hsun Hsu, Minseon Gwak, Soon Hoe Lim +2Dynamical Systems Flow Matching
Georgia Institute of Technology · Lawrence Berkeley National Lab · International Computer Science Institute +2
TRACE: A Reproducible Benchmark for Electricity Price Forecasting with Official Operational Text
Sep 30, 2026Xinyi Yi, Zhangdie Yuan, Ioannis LestasElectricity Price Forecasting Time Series Forecasting
Department of Engineering, University of Cambridge · Department of Computer Science, University of Cambridge · University of Cambridge

Oct 23, 2026J/K move · Enter open · S save

When to Trust Memory: Retrieval-Guided Probabilistic Spatiotemporal Forecasting under Distribution Shift

Haochen Lv, Jianhao Zhang, Zhichen Lei, Yang Jiang+4

School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China. · Key Laboratory of Big Data and Artificial Intelligence in Transportation, Ministry of Education, Beijing, China. · Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing, China.

Spatiotemporal Forecasting Probabilistic Forecasting Distribution Shift Robustness Temporal Distribution Shift Retrieval-Augmented Time Series Forecasting

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Spatiotemporal forecasting plays an important role in real-world applications, where probabilistic approaches provide uncertainty estimates for decision-making. Real-world systems are inherently non-stationary, leading to distribution shifts between training and test data. However, existing probabilistic methods mainly focus on modeling data distributions under standard settings, while distribution shift methods often handle such shifts in a coarse manner, failing to characterize shift degree and adapt prediction strategy accordingly. As a result, they often produce miscalibrated and unreliable forecasts. To address this issue, we propose REMIND, a retrieval-guided probabilistic framework for spatiotemporal forecasting under distribution shift. REMIND leverages retrieval to estimate how well current observation are supported by historical patterns, enabling adaptive prediction under different shift regimes. It employs spatiotemporal retrieval to construct a memory bank and introduces a dual branch architecture, combining a memory-calibrated probabilistic branch for mild shifts with a deviation-aware probabilistic branch for severe shifts. A shift-adaptive gate, guided by retrieval similarity and memory dispersion, balances the two branches according to shift degree. We further design a hierarchical Gaussian mixture to capture heterogeneous uncertainty over the dual branch predictions, and introduce a memory perturbation training scheme to improve generalization. Experiments on real-world datasets with distribution shifts demonstrate that REMIND consistently outperforms state-of-the-art methods in both forecasting accuracy and probabilistic prediction quality.