Predicting Solar Energetic Particle Events with Time Series Shapelets
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 ev...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/ada601 |
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| author | Omar Bahri Peiyu Li Soukaïna Filali Boubrahimi Shah Muhammad Hamdi |
| author_facet | Omar Bahri Peiyu Li Soukaïna Filali Boubrahimi Shah Muhammad Hamdi |
| author_sort | Omar Bahri |
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| description | 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. |
| format | Article |
| id | doaj-art-2f4c2562072049d0bcd14684649d4af0 |
| institution | Kabale University |
| issn | 1538-4357 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj-art-2f4c2562072049d0bcd14684649d4af02025-02-07T08:55:07ZengIOP PublishingThe Astrophysical Journal1538-43572025-01-01980112810.3847/1538-4357/ada601Predicting Solar Energetic Particle Events with Time Series ShapeletsOmar Bahri0https://orcid.org/0009-0001-6961-3046Peiyu Li1https://orcid.org/0000-0002-0563-1050Soukaïna Filali Boubrahimi2https://orcid.org/0000-0001-5693-6383Shah Muhammad Hamdi3https://orcid.org/0000-0002-9303-7835Department of Computer Science, Utah State University , Logan, UT 84322, USA ; omar.bahri@usu.edu, peiyu.li@usu.edu, soukaina.boubrahimi@usu.edu, s.hamdi@usu.eduDepartment of Computer Science, Utah State University , Logan, UT 84322, USA ; omar.bahri@usu.edu, peiyu.li@usu.edu, soukaina.boubrahimi@usu.edu, s.hamdi@usu.eduDepartment of Computer Science, Utah State University , Logan, UT 84322, USA ; omar.bahri@usu.edu, peiyu.li@usu.edu, soukaina.boubrahimi@usu.edu, s.hamdi@usu.eduDepartment of Computer Science, Utah State University , Logan, UT 84322, USA ; omar.bahri@usu.edu, peiyu.li@usu.edu, soukaina.boubrahimi@usu.edu, s.hamdi@usu.eduSolar 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.https://doi.org/10.3847/1538-4357/ada601Solar energetic particlesTime series analysisSpace weather |
| spellingShingle | Omar Bahri Peiyu Li Soukaïna Filali Boubrahimi Shah Muhammad Hamdi Predicting Solar Energetic Particle Events with Time Series Shapelets The Astrophysical Journal Solar energetic particles Time series analysis Space weather |
| title | Predicting Solar Energetic Particle Events with Time Series Shapelets |
| title_full | Predicting Solar Energetic Particle Events with Time Series Shapelets |
| title_fullStr | Predicting Solar Energetic Particle Events with Time Series Shapelets |
| title_full_unstemmed | Predicting Solar Energetic Particle Events with Time Series Shapelets |
| title_short | Predicting Solar Energetic Particle Events with Time Series Shapelets |
| title_sort | predicting solar energetic particle events with time series shapelets |
| topic | Solar energetic particles Time series analysis Space weather |
| url | https://doi.org/10.3847/1538-4357/ada601 |
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