基于 Slim-TSF 的混合嵌入式特征选择与分类方法研究
Towards Hybrid Embedded Feature Selection and Classification Approach with Slim-TSF
AI总结：
本文改进 Slim-TSF 多元时间序列分类框架，引入系统化评估与嵌入式特征选择，以刻画太阳耀斑及其源区的演化特征；对比实验显示 TSS 与 HSS 平均均提升 5%。
摘要：
传统的太阳耀斑预报方法大多依赖基于物理或数据驱动的模型，并使用太阳磁图，将耀斑预测视为一个时间点分类问题。这种方法存在局限性，尤其是在捕捉太阳活动的演化性质方面。鉴于传统耀斑预报方法的这些局限，本研究旨在揭示太阳耀斑及其源区之间隐藏的关系和演化特征。我们此前提出的 Sliding Window Multivariate Time Series Forest（Slim-TSF，滑动窗口多元时间序列森林）已证明其应用于多元时间序列数据的可行性。本研究的一个重要方面是将更新后的 Slim-TSF 框架与原始模型的结果进行比较分析。初步结果表明，性能有显著提升：True Skill Statistic（TSS，真实技巧统计量）和 Heidke Skill Score（HSS，海德克技巧评分）平均均提高了 5%。这一提升不仅凸显了我们改进方法的有效性，也表明我们的系统化评估与特征选择方法能够显著提高太阳耀斑预报模型的预测精度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
