Solar flare prediction using multivariate time series decision trees

• Published in: 2017 IEEE International Conference on Big Data (Big Data) pp. 2569 - 2578
• Main Authors: Ruizhe Ma, Boubrahimi, Soukaina Filali, Hamdi, Shah Muhammad, Angryk, Rafal A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2017
• Subjects: Decision trees; Earth; Magnetic separation; Meteorology; Satellites; Solar Flare; Time series analysis; time series decision trees; univariate clustering
• DOI: 10.1109/BigData.2017.8258216

Space Weather is of rising importance in scientific discipline that describes the way in which the Sun and space impact a myriad of activities down on Earth as well as the safety of the space crew members on board of the space stations. Consequently, it is imperative to better quantify the risk of future space weather events. Most of the flare prediction models in literature use physical parameters of the potentially flaring active regions during a limited interval to gain insights on whether a flare will happen or not. This limits our perception of how an event evolves for an extended duration across multiple parameters. In this paper we followed a data-driven approach to address the problem of flare prediction from a multivariate time series analysis perspective and attempt to cluster potential flaring active regions by applying Distance Density clustering on individual parameters and further organize the clustering results into a multivariate time series decision tree. We compared different data extraction priors and spans, and ranked the importance for different parameters through univariate clustering. To the best of our knowledge, this is the first attempt to predict solar flares using a tree structure.