EXCON：用于太阳耀斑预测的严重不平衡多元时间序列的极端基于实例的对比表示学习
EXCON: Extreme Instance-based Contrastive Representation Learning of Severely Imbalanced Multivariate Time Series for Solar Flare Prediction
AI总结：
本文提出对比表示学习框架EXCON，通过提取核心特征、选择对比表示、利用定制极端重建损失训练嵌入模块以解决太阳耀斑预测中的严重类别不平衡问题，提升了多元时间序列的分类性能。
摘要：
在日球物理学研究中，预测太阳耀斑至关重要，因为它们可能会对天基系统和地球基础设施产生重大影响。太阳成像天文台记录的太阳活动区磁场数据被转换为多元时间序列，以便通过基于时间窗口的分析进行太阳耀斑预测。在由多元时间序列驱动的太阳耀斑预测领域，通过有效的多元时间序列表示学习策略来解决严重的类别不平衡问题，是开发稳健预测模型的关键。传统方法在重大太阳耀斑罕见的预测任务中，往往难以应对对多数类过拟合的问题。本工作提出了EXCON，这是一个对比表示学习框架，旨在在此类不平衡情况下提升分类性能。EXCON通过四个阶段运行：从多元时间序列数据中获取核心特征；为每个类别选择独特的对比表示以最大化类间分离；使用定制的极端重建损失训练时间特征嵌入模块以最小化类内差异；并将分类器应用于学习到的嵌入以进行稳健分类。该方法利用对比学习原理，将相似实例在特征空间中映射得更近，同时使不相似实例相距更远，这一策略在太阳耀斑预测任务中尚未得到广泛探索。该方法不仅解决了类别不平衡问题，还提供了一种通用解决方案，适用于跨二元和多类分类问题的单变量和多元时间序列。实验结果，包括在基准太阳耀斑数据集以及带有二元和多类标签的多个时间序列存档数据集上的评估，证明了EXCON在提升分类性能方面的有效性。
英文摘要：
In heliophysics research, predicting solar flares is crucial due to their potential to impact both space-based systems and Earth's infrastructure substantially. Magnetic field data from solar active regions, recorded by solar imaging observatories, are transformed into multivariate time series to enable solar flare prediction using temporal window-based analysis. In the realm of multivariate time series-driven solar flare prediction, addressing severe class imbalance with effective strategies for multivariate time series representation learning is key to developing robust predictive models. Traditional methods often struggle with overfitting to the majority class in prediction tasks where major solar flares are infrequent. This work presents EXCON, a contrastive representation learning framework designed to enhance classification performance amidst such imbalances. EXCON operates through four stages: obtaining core features from multivariate time series data; selecting distinctive contrastive representations for each class to maximize inter-class separation; training a temporal feature embedding module with a custom extreme reconstruction loss to minimize intra-class variation; and applying a classifier to the learned embeddings for robust classification. The proposed method leverages contrastive learning principles to map similar instances closer in the feature space while distancing dissimilar ones, a strategy not extensively explored in solar flare prediction tasks. This approach not only addresses class imbalance but also offers a versatile solution applicable to univariate and multivariate time series across binary and multiclass classification problems. Experimental results, including evaluations on the benchmark solar flare dataset and multiple time series archive datasets with binary and multiclass labels, demonstrate EXCON's efficacy in enhancing classification performance.
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