Multivariate Time Series-based Solar Flare Prediction by Functional Network Embedding and Sequence Modeling
Multivariate Time Series-based Solar Flare Prediction by Functional Network Embedding and Sequence Modeling
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发表时间：
2022
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通讯作者：
S. M. Hamdi;Abu Fuad Ahmad;S. F. Boubrahimi
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作者：
S. M. Hamdi;Abu Fuad Ahmad;S. F. Boubrahimi
太阳上的重大耀斑事件可能对空间和地面基础设施产生危险影响。利用太阳活动区磁场参数的多变量时间序列（MVTS）是预测太阳活动区（AR）在一段时间后可能发生耀斑的一种有效方法。现有的基于MVTS的耀斑预测模型是基于训练传统的分类器与单变量时间序列实例的预设统计特征，或训练基于递归神经网络（RNN）或长短期记忆（LSTM）网络的深度序列模型。虽然早期的方法受到手工设计特征的影响，但后一种方法仅使用MVTS实例的时间维度。MVTS的变量不仅取决于其历史值，还取决于其他变量。在这项工作中，我们使用MVTS实例的动态函数网络表示，通过图形卷积网络（GCN）嵌入来利用变量的高阶关系。除了通过函数网络嵌入找到空间（变量间）模式外，我们的模型还通过LSTM网络使用局部和全局时间模式。我们在真实太阳耀斑数据集上的实验显示出比其他基线方法更好的预测性能。
Major flaring events on the Sun can have hazardous impacts on both space and ground-based infrastructure. An effective approach of predicting that a solar active region (AR) is likely to flare after a period of time is to leverage multivariate time series (MVTS) of the AR magnetic field parameters. Existing MVTS-based flare prediction models are based on training traditional classifiers with preset statistical features of univariate time series instances, or training deep sequence models based on Recurrent Neural Network (RNN) or Long Short Term Memory (LSTM) Network. While the earlier approach is affected by hand-engineered features, the latter approach uses only the temporal dimension of the MVTS instances. The variables of MVTS do not depend only on their historical values but also on other variables. In this work, we used the dynamic functional network representation of the MVTS instances to leverage higher-order relationships of the variables through Graph Convolution Network (GCN) embedding. In addition to finding spatial (inter-variable) patterns through functional network embedding, our model uses local and global temporal patterns through LSTM networks. Our experiments on a real-life solar flare dataset exhibit better prediction performance than other baseline methods.