Date of Award:
8-2026
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Physics
Committee Chair(s)
Soukaina Filali Boubrahimi
Committee
Soukaina Filali Boubrahimi
Committee
Jan Sojka
Committee
Jim Wheeler
Abstract
Solar flares are the largest explosions in the solar system; they are caused by changes in the Sun’s magnetic field. Strong solar flares can disrupt power systems, damage satellites, and interfere with radio communication, so improving flare prediction is important. This thesis develops a way to predict severe solar flares while also helping researchers understand why those predictions are made. The approach looks for short patterns in solar magnetic field data that are linked to future flare activity. It then studies how these patterns appear together and in what order they happen over time. By doing this, the research not only helps identify when a solar flare may occur, but also provides clues about the magnetic behavior that leads to it. Overall, this work offers a prediction method that is both useful and easier to interpret than many existing machine learning approaches.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Watson, Drew, "Mining Time Series Shapelets and Association Rules for Solar Flare Prediction" (2026). All Graduate Theses and Dissertations, Fall 2023 to Present. 827.
https://digitalcommons.usu.edu/etd2023/827
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