Uncertainty-Aware Solar Flare Regression

2026-03-05

AI Summary

This study addresses the high false alarm rate in solar flare prediction, which stems from inadequate uncertainty quantification—particularly problematic given the scarcity of extreme-event data. To this end, the authors propose a novel regression framework that integrates deep learning with rigorous uncertainty estimation, introducing conformalized quantile regression (CQR) to space weather forecasting for the first time. Leveraging full-disk magnetogram data, they systematically evaluate four pretrained models combined with three uncertainty quantification methods. Experimental results demonstrate that the proposed approach significantly narrows prediction interval lengths while maintaining valid coverage, outperforming existing techniques and thereby enhancing both the reliability and practical utility of solar flare forecasts.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Current solar flare predictions often lack precise quantification of their reliability, resulting in frequent false alarms, particularly when dealing with datasets skewed towards extreme events. To improve the trustworthiness of space weather forecasting, it is crucial to establish confidence intervals for model predictions. Conformal prediction, a machine learning framework, presents a promising avenue for this purpose by constructing prediction intervals that ensure valid coverage in finite samples without making assumptions about the underlying data distribution. In this study, we explore the application of conformal prediction to regression tasks in space weather forecasting. Specifically, we implement full-disk solar flare prediction using images created from magnetic field maps and adapt four pre-trained deep learning models to incorporate three distinct methods for constructing confidence intervals: conformal prediction, quantile regression, and conformalized quantile regression. Our experiments demonstrate that conformalized quantile regression achieves higher coverage rates and more favorable average interval lengths compared to alternative methods, underscoring its effectiveness in enhancing the reliability of solar weather forecasting models.

Problem

• solar flare prediction
• uncertainty quantification
• conformal prediction
• space weather forecasting
• confidence intervals

Innovation

• conformal prediction
• quantile regression
• solar flare forecasting
• uncertainty quantification
• deep learning