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Abstract Accurate forecasting of solar flares is crucial for mitigating their severe impacts on space-based and communication systems. Deep learning models have shown promise in predicting flare activity using magnetic field measurements of solar active regions. However, the scarcity and imbalance of flare occurrence data—particularly for major flares—pose significant challenges to model robustness and generalization. This study provides a comprehensive evaluation of time-series data augmentation techniques to improve deep learning-based solar flare prediction. Using the benchmark Space Weather Analytics for Solar Flares data set, which offers multivariate magnetic field parameter time series, we assess 12 augmentation methods across three architectures: fully convolutional network (FCN), multivariate LSTM-FCN, and residual network. Our primary experiments address the binary classification of major (M- and X-class) versus minor (C-, B-, and FQ-class) flares, using metrics tailored for imbalanced data, including recall, true skill statistic, Heidke skill score, and the Gini coefficient. A case study on X versus M classification further examines performance under a simpler, more balanced setting. Results show that select augmentation strategies yield measurable gains across different models and scenarios, offering a viable path forward for addressing data scarcity in space weather forecasting tasks.
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Solar Flare Forecasting with Deep Learning-based Time Series Classifiers
Over the past two decades, machine learning and deep learning techniques for forecasting solar flares have generated great impact due to their ability to learn from a high dimensional data space. However, lack of high quality data from flaring phenomena becomes a constraining factor for such tasks. One of the methods to tackle this complex problem is utilizing trained classifiers with multivariate time series of magnetic field parameters. In this work, we compare the exceedingly popular multivariate time series classifiers applying deep learning techniques with commonly used machine learning classifiers (i.e., SVM). We intend to explore the role of data augmentation on time series oriented flare prediction techniques, specifically the deep learning-based ones. We utilize four time series data augmentation techniques and couple them with selected multivariate time series classifiers to understand how each of them affects the outcome. In the end, we show that the deep learning algorithms as well as augmentation techniques improve our classifiers performance. The resulting classifiers’ performance after augmentation outplayed the traditional flare forecasting techniques.
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- Award ID(s):
- 1931555
- PAR ID:
- 10402089
- Date Published:
- Journal Name:
- 2022 26th International Conference on Pattern Recognition (ICPR)
- Page Range / eLocation ID:
- 2907 to 2913
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1109/ICPR56361.2022.9956097
