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Abstract Solar energetic particles (SEPs) are associated with extreme solar events that can cause major damage to space- and ground-based life and infrastructure. High-intensity SEP events, particularly ∼100 MeV SEP events, can pose severe health risks for astronauts owing to radiation exposure and affect Earth’s orbiting satellites (e.g., Landsat and the International Space Station). A major challenge in the SEP event prediction task is the lack of adequate SEP data because of the rarity of these events. In this work, we aim to improve the prediction of ∼30, ∼60, and ∼100 MeV SEP events by synthetically increasing the number of SEP samples. We explore the use of a univariate and multivariate time series of proton flux data as input to machine-learning-based prediction methods, such as time series forest (TSF). Our study covers solar cycles 22, 23, and 24. Our findings show that using data augmentation methods, such as the synthetic minority oversampling technique, remarkably increases the accuracy and F1-score of the classifiers used in this research, especially for TSF, where the average accuracy increased by 20%, reaching around 90% accuracy in the ∼100 MeV SEP prediction task. We also achieved higher prediction accuracy when using the multivariate time series data of the proton flux. Finally, we build a pipeline framework for our best-performing model, TSF, and provide a comprehensive hierarchical classification of the ∼100, ∼60, and ∼30 MeV and non-SEP prediction scenarios.
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This content will become publicly available on February 7, 2026
Predicting Solar Energetic Particle Events with Time Series Shapelets
Abstract Solar energetic particle (SEP) events pose significant risks to both space and ground-level infrastructure, as well as to human health in space. Understanding and predicting these events are critical for mitigating their potential impacts. In this paper, we address the challenge of predicting SEP events using proton flux data. We leverage some of the most recent advances in time series data mining, such as shapelets and the matrix profile, to propose a simple and easily understandable prediction approach. Our objective is to mitigate the interpretability challenges inherent to most machine learning models and to show that other methods exist that can not only yield accurate forecasts but also facilitate exploration and insight generation within the data domain. For this purpose, we construct a multivariate time series data set consisting of proton flux data recorded by the National Oceanic and Atmospheric Administration's geosynchronous orbit Earth-observing satellite. Then, we use our proposed approach to mine shapelets and make predictions using a random forest classifier. We demonstrate that our approach rivals state-of-the-art SEP prediction, offering superior interpretability and the ability to predict SEP events before their parent eruptive flares.
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- PAR ID:
- 10582814
- Publisher / Repository:
- American Astronomical Society (AAS)
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 980
- Issue:
- 1
- ISSN:
- 0004-637X
- Page Range / eLocation ID:
- 128
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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