面向极端不平衡多元时间序列数据的太阳耀斑预测对比表示学习
Contrastive Representation Learning for Predicting Solar Flares from Extremely Imbalanced Multivariate Time Series Data
AI总结：
本文提出CONTREX对比表示学习方法，通过提取动态特征、构建正负类极值点并以对比重构损失训练嵌入模块，解决多元时间序列极端类别不平衡问题，在SWAN-SF数据集上提升了太阳耀斑预测性能。
摘要：
大太阳耀斑是太阳磁通量的突然增强，对技术基础设施构成重大风险。鉴于此，通过机器学习方法从太阳活动区磁场数据中有效预测大耀斑在空间天气研究中变得非常重要。磁场数据可以表示为多元时间序列模态，其中由于大耀斑事件的稀有性，数据呈现出极端的类别不平衡。在基于时间序列分类的耀斑预测中，对比表示学习方法的使用相对有限。在本文中，我们提出了CONTREX，一种新颖的面向多元时间序列数据的对比表示学习方法，以应对时间依赖性和极端类别不平衡的挑战。我们的方法包括从多元时间序列实例中提取动态特征，从正类和负类特征向量中推导出两个能够提供最大分离能力的极值点，并在我们新颖的对比重构损失的指导下，利用原始多元时间序列数据训练一个序列表示嵌入模块，以生成与极值点对齐的嵌入。这些嵌入捕获了关键的时间序列特征并增强了判别能力。我们的方法在太阳耀斑空间天气分析（SWAN-SF）多元时间序列基准数据集上，相对于基线方法展示了有前景的太阳耀斑预测结果。
英文摘要：
Major solar flares are abrupt surges in the Sun's magnetic flux, presenting significant risks to technological infrastructure. In view of this, effectively predicting major flares from solar active region magnetic field data through machine learning methods becomes highly important in space weather research. Magnetic field data can be represented in multivariate time series modality where the data displays an extreme class imbalance due to the rarity of major flare events. In time series classification-based flare prediction, the use of contrastive representation learning methods has been relatively limited. In this paper, we introduce CONTREX, a novel contrastive representation learning approach for multivariate time series data, addressing challenges of temporal dependencies and extreme class imbalance. Our method involves extracting dynamic features from the multivariate time series instances, deriving two extremes from positive and negative class feature vectors that provide maximum separation capability, and training a sequence representation embedding module with the original multivariate time series data guided by our novel contrastive reconstruction loss to generate embeddings aligned with the extreme points. These embeddings capture essential time series characteristics and enhance discriminative power. Our approach shows promising solar flare prediction results on the Space Weather Analytics for Solar Flares (SWAN-SF) multivariate time series benchmark dataset against baseline methods.
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