改进时间序列森林模型，提升太阳耀斑预测准确率
Towards Hybrid Embedded Feature Selection and Classification Approach with Slim-TSF
- 基于滑动窗口的多变量时序森林，捕捉耀斑演化特征
- TSS和HSS平均提升5%，验证方法有效性
- 适合关注空间天气预测与时序建模的研究者
传统太阳耀斑预测多依赖物理或数据驱动模型，以太阳磁图作为输入，将耀斑预测视为瞬时分类问题，难以捕捉太阳活动的动态演变。针对此局限，本研究旨在揭示耀斑及其源区的隐藏关系与演化特性。此前提出的滑动窗口多变量时间序列森林（Slim-TSF）已证明在多变量时序数据上的可行性。本研究对更新后的Slim-TSF框架与原始模型进行对比分析，初步结果表明，TSS和Heidke Skill Score（HSS）平均分别提升5%。该提升不仅验证了优化方法的有效性，也表明系统化评估与特征选择可显著提高太阳耀斑预测模型的准确性。
原文摘要 · Abstract (English)
Traditional solar flare forecasting approaches have mostly relied on physics-based or data-driven models using solar magnetograms, treating flare predictions as a point-in-time classification problem. This approach has limitations, particularly in capturing the evolving nature of solar activity. Recognizing the limitations of traditional flare forecasting approaches, our research aims to uncover hidden relationships and the evolutionary characteristics of solar flares and their source regions. Our previously proposed Sliding Window Multivariate Time Series Forest (Slim-TSF) has shown the feasibility of usage applied on multivariate time series data. A significant aspect of this study is the comparative analysis of our updated Slim-TSF framework against the original model outcomes. Preliminary findings indicate a notable improvement, with an average increase of 5\% in both the True Skill Statistic (TSS) and Heidke Skill Score (HSS). This enhancement not only underscores the effectiveness of our refined methodology but also suggests that our systematic evaluation and feature selection approach can significantly advance the predictive accuracy of solar flare forecasting models.
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