通过多方面预处理与对比学习增强基于多元时间序列的太阳耀斑预测
Enhancing Multivariate Time Series-based Solar Flare Prediction with Multifaceted Preprocessing and Contrastive Learning
AI总结：
本研究提出了一种新颖的预处理流程和结合对比学习与GRU回归模型的ContReg分类器，用于基于多元时间序列的太阳耀斑预测，显著提升了TSS得分并超越了现有方法。
摘要：
由于强烈的太阳耀斑对宇航员、太空设备和卫星通信系统构成重大风险，准确的太阳耀斑预测至关重要。我们的研究通过对基于光球磁场参数的多元时间序列数据集应用先进的数据预处理和分类方法，提升了太阳耀斑预测能力。首先，本研究采用了一种新颖的预处理流程，包括缺失值插补、归一化、平衡采样、近决策边界样本移除以及特征选择，以显著提升预测准确率。其次，我们将对比学习与GRU回归模型相结合，开发了一种名为ContReg的新颖分类器，该分类器采用双重学习方法，从而进一步增强了预测性能。为了验证预处理流程的有效性，我们比较并展示了每个步骤带来的性能提升；为了展示ContReg分类器的有效性，我们将其性能与基于序列的深度学习架构、机器学习模型以及先前研究的结果进行了比较。我们的结果展示了极佳的真实技能统计量（TSS）得分，超越了以往的方法，凸显了精确的数据预处理和分类器开发在基于时间序列的太阳耀斑预测中的关键作用。
英文摘要：
Accurate solar flare prediction is crucial due to the significant risks that intense solar flares pose to astronauts, space equipment, and satellite communication systems. Our research enhances solar flare prediction by utilizing advanced data preprocessing and classification methods on a multivariate time series-based dataset of photospheric magnetic field parameters. First, our study employs a novel preprocessing pipeline that includes missing value imputation, normalization, balanced sampling, near decision boundary sample removal, and feature selection to significantly boost prediction accuracy. Second, we integrate contrastive learning with a GRU regression model to develop a novel classifier, termed ContReg, which employs dual learning methodologies, thereby further enhancing prediction performance. To validate the effectiveness of our preprocessing pipeline, we compare and demonstrate the performance gain of each step, and to demonstrate the efficacy of the ContReg classifier, we compare its performance to that of sequence-based deep learning architectures, machine learning models, and findings from previous studies. Our results illustrate exceptional True Skill Statistic (TSS) scores, surpassing previous methods and highlighting the critical role of precise data preprocessing and classifier development in time series-based solar flare prediction.
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。
