A time series classification-based approach for solar flare prediction

• Published in: 2017 IEEE International Conference on Big Data (Big Data) pp. 2543 - 2551
• Main Authors: Hamdi, Shah Muhammad, Kempton, Dustin, Ruizhe Ma, Boubrahimi, Soukaina Filali, Angryk, Rafal A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2017
• Subjects: Data models; Flare prediction; k-NN classifier; Predictive models; Sun; Support vector machines; Time series; Time series analysis; Total unsigned current helicity
• DOI: 10.1109/BigData.2017.8258213

Solar flare prediction is an important task because of their potential impacts on both space and terrestrial infrastructure. This prediction task can be modeled as a binary classification between flaring and non-flaring Active Regions. Previous works on flare prediction focused on representing flaring and non-flaring Active Region examples in vector space, where the feature space was found from the Active Region magnetic field parameters. We extract time series samples of these Active Region parameters and present a flare prediction method based on the k-NN classification of the univariate time series. We find that, for our classification task, using a statistical summarization on the time series of a single Active Region parameter, called total unsigned current helicity, outperforms the use of all Active Region parameters at a single instant of time. Additionally, we present a data model of the flaring/non-flaring Active Regions using multivariate time series.