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Title: 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.  more » « less
Award ID(s):
1931555
NSF-PAR ID:
10402089
Author(s) / Creator(s):
; ; ;
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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