Image de couverture de livre

Stabilizing distribution-free probabilistic forecasts

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Van Belle, Jente, Wen, Honglin, Verbeke, Wouter, Pinson, Pierre
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Estimating Treatment Effects in Networks using Domain Adversarial Training
par: Caljon, Daan, et autres
Publié: (2025)

Using dynamic loss weighting to boost improvements in forecast stability
par: Caljon, Daan, et autres
Publié: (2024)

Value-oriented forecast reconciliation for renewables in electricity markets
par: Wen, Honglin, et autres
Publié: (2025)

Optimizing Treatment Allocation in the Presence of Interference
par: Caljon, Daan, et autres
Publié: (2024)

Decision-centric fairness: Evaluation and optimization for resource allocation problems
par: De Vos, Simon, et autres
Publié: (2025)

Decision-calibrated prediction sets for robust power system operations
par: Stratigakos, Akylas, et autres
Publié: (2026)

Marginalize, Rather than Impute: Probabilistic Wind Power Forecasting with Incomplete Data
par: Wen, Honglin, et autres
Publié: (2024)

Uncertainty in Machine Learning
par: Weytjens, Hans, et autres
Publié: (2025)

Is TabPFN the Silver Bullet for Insurance Pricing?
par: Deprez, Bruno, et autres
Publié: (2026)

How to Purchase Labels? A Cost-Effective Approach Using Active Learning Markets
par: Huang, Xiwen, et autres
Publié: (2025)

Probabilistic Prediction Markets with Intermittent Contributions
par: Vitali, Michael, et autres
Publié: (2025)

Data is missing again -- Reconstruction of power generation data using $k$-Nearest Neighbors and spectral graph theory
par: Pierrot, Amandine, et autres
Publié: (2024)

Metalearners for Ranking Treatment Effects
par: Vanderschueren, Toon, et autres
Publié: (2024)

Pooling Probabilistic Forecasts for Cooperative Wind Power Offering
par: Wen, Honglin, et autres
Publié: (2025)

nabqr: Python package for improving probabilistic forecasts
par: Jørgensena, Bastian Schmidt, et autres
Publié: (2025)

Inductive inference of gradient-boosted decision trees on graphs for insurance fraud detection
par: Vandervorst, Félix, et autres
Publié: (2025)

Uplift modeling with continuous treatments: A predict-then-optimize approach
par: De Vos, Simon, et autres
Publié: (2024)

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts
par: Duchemin, Quentin, et autres
Publié: (2025)

Bayesian Regression Markets
par: Falconer, Thomas, et autres
Publié: (2023)

GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering
par: Deprez, Bruno, et autres
Publié: (2025)

Enforcing tail calibration when training probabilistic forecast models
par: Wessel, Jakob Benjamin, et autres
Publié: (2025)

Machine learning-based probabilistic forecasting of solar irradiance in Chile
par: Baran, Sándor, et autres
Publié: (2024)

Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimation
par: Bockel-Rickermann, Christopher, et autres
Publié: (2024)

The foundations of cost-sensitive causal classification
par: Verbeke, Wouter, et autres
Publié: (2020)

Skillful joint probabilistic weather forecasting from marginals
par: Alet, Ferran, et autres
Publié: (2025)

Clustering of timed sequences -- Application to the analysis of care pathways
par: Guyet, Thomas, et autres
Publié: (2024)

Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review
par: Deprez, Bruno, et autres
Publié: (2025)

Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation
par: Deprez, Bruno, et autres
Publié: (2024)

Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression
par: Lipiecki, Arkadiusz, et autres
Publié: (2024)

On-line conformalized neural networks ensembles for probabilistic forecasting of day-ahead electricity prices
par: Brusaferri, Alessandro, et autres
Publié: (2024)

NBMLSS: probabilistic forecasting of electricity prices via Neural Basis Models for Location Scale and Shape
par: Brusaferri, Alessandro, et autres
Publié: (2024)

Hybrid additive modeling with partial dependence for supervised regression and dynamical systems forecasting
par: Claes, Yann, et autres
Publié: (2023)

An intuitive rearranging of the Yates covariance decomposition for probabilistic verification of forecasts with the Brier score
par: Vieira, Bruno Hebling
Publié: (2026)

Using representation balancing to learn conditional-average dose responses from clustered data
par: Bockel-Rickermann, Christopher, et autres
Publié: (2023)

CARE: Confidence-Aware Regression Estimation of building density fine-tuning EO Foundation Models
par: Dionelis, Nikolaos, et autres
Publié: (2025)

Do we actually understand the impact of renewables on electricity prices? A causal inference approach
par: Cacciarelli, Davide, et autres
Publié: (2025)

Improving probabilistic forecasts of extreme wind speeds by training statistical post-processing models with weighted scoring rules
par: Wessel, Jakob Benjamin, et autres
Publié: (2024)

Aggregating distribution forecasts from deep ensembles
par: Schulz, Benedikt, et autres
Publié: (2022)

Privacy-Aware Data Acquisition under Data Similarity in Regression Markets
par: Pandey, Shashi Raj, et autres
Publié: (2023)

Evolving Multi-Scale Normalization for Time Series Forecasting under Distribution Shifts
par: Qin, Dalin, et autres
Publié: (2024)