Feature Selection from Multivariate Time Series Data: A Case Study of Solar Flare Prediction
Feature Selection from Multivariate Time Series Data: A Case Study of Solar Flare Prediction
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DOI：
10.1109/bigdata55660.2022.10020669
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Khaznah Alshammari;S. M. Hamdi;S. F. Boubrahimi
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文献类型：
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作者：
Khaznah Alshammari;S. M. Hamdi;S. F. Boubrahimi
太阳物理学家经常使用太阳磁场参数来分析和预测太阳事件。磁场参数的时间观测，即，多变量时间序列（MVTS）表示便于找到磁场状态与极端太阳事件发生的关系（例如，太阳耀斑）。MVTS表示的太阳磁场参数（特征）的特征选择可以选择给出高预测精度的最相关的参数。在本文中，我们提出了一种基于深度学习的特征选择方法，更具体地说，一种基于LSTM的增量特征选择方法，作为MVTS数据中特征选择的端到端解决方案。我们分两步对多变量时间序列数据进行基于LSTM的特征选择。首先，每个MVTS特征由基于LSTM的单变量序列分类器单独评估，其次，将表现最好的特征组合以产生用于下游基于LSTM的多变量序列分类器的输入。我们比较了建议的MVTS特征选择方法与其他三个基线特征选择方法的MVTS为基础的太阳耀斑预测数据集，并证明我们的方法选择更多的歧视性特征相比，其他方法。
Solar physicists frequently use solar magnetic field parameters for analyzing and predicting solar events. Temporal observation of magnetic field parameters, i.e., multivariate time series (MVTS) representation facilitates finding relationships of magnetic field states to the occurrence of extreme solar events (e.g., solar flares). Feature selection of MVTS-represented solar magnetic field parameters (features) can select the most relevant parameters that give high prediction accuracy. In this paper, we propose a deep learning-based feature selection method, more specifically, an LSTM-based incremental feature selection method, as an end-to-end solution for feature selection in MVTS data. We performed LSTM-based feature selection for multivariate time series data in two steps. Firstly, each MVTS feature is evaluated individually by an LSTM-based univariate sequence classifier, and secondly, the top-performing features are combined to produce input for a downstream LSTM-based multivariate sequence classifier. We compared the proposed MVTS feature selection method with three other baseline feature selection methods on an MVTS-based solar flare prediction dataset and demonstrated that our method selects more discriminatory features compared to other methods.