用新损失函数提升太阳耀斑72小时预测准确率
FLARE-SSM: Deep State Space Models with Influence-Balanced Loss for 72-Hour Solar Flare Prediction
- 基于深度状态空间模型，融合多波段太阳图像
- 在11年周期数据上，显著提升评分与可靠性指标
- 特别适合处理耀斑类别严重不平衡的问题
准确可靠的太阳耀斑预测对减轻对关键基础设施的影响至关重要。然而，当前太阳耀斑预报性能仍不理想。本文针对未来72小时内最大耀斑类别的预测任务，提出一种基于多重深度状态空间模型的预测方法。针对耀斑类别间严重的不平衡问题，引入频率与局部边界感知的可靠性损失（FLARE损失），以提升预测性能与可靠性。实验在覆盖完整11年太阳活动周期的多波段太阳图像数据集上进行。结果表明，该方法在标准评估指标——Gandin-Murphy-Gerrity得分与真实技能统计量上均优于基线模型。
原文摘要 · Abstract (English)
Accurate and reliable solar flare predictions are essential to mitigate potential impacts on critical infrastructure. However, the current performance of solar flare forecasting is insufficient. In this study, we address the task of predicting the class of the largest solar flare expected to occur within the next 72 hours. Existing methods often fail to adequately address the severe class imbalance across flare classes. To address this issue, we propose a solar flare prediction model based on multiple deep state space models. In addition, we introduce the frequency & local-boundary-aware reliability loss (FLARE loss) to improve predictive performance and reliability under class imbalance. Experiments were conducted on a multi-wavelength solar image dataset covering a full 11-year solar activity cycle. As a result, our method outperformed baseline approaches in terms of both the Gandin-Murphy-Gerrity score and the true skill statistic, which are standard metrics in terms of the performance and reliability.
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