Short-term Classification of Strong Solar Energetic Particle Events using Multivariate Time Series Classifiers

Solar energetic particle (SEP) events are one of the most crucial aspects of space weather that require continuous monitoring and forecasting. Their prediction depends on various factors including source eruptions. In the present work, we use the Geostationary Solar Energetic Particle (GSEP) data set covering solar cycles 22, 23, and 24. We develop a framework using time series-based machine learning (ML) models with the aim of developing robust short-term forecasts by classifying SEP events. For this purpose, we introduce an ensemble learning approach that merges the results from univariate time series of three proton channels (10 MeV, 50 MeV, and 100 MeV) and the long band X-ray flux channel from the Geostationary Operational Environmental Satellite (GOES) missions and analyze their performance. We consider three models, namely, time series forest (TSF), supervised time series forest (STSF) and bag of SFA symbols (BOSS). Our study also focuses on understanding and developing confidence in the predictive capabilities of our models. Therefore, we utilize multiple evaluation techniques and metrics. Based on that, we find STSF to perform well in all scenarios. The summary of metrics for the STSF model is as follows: AUC = 0.981; F1-score = 0.960; TSS = 0.919; HSS = 0.920; GSS = 0.852; and MCC = 0.920. The Brier score loss of the STSF model is 0.077. This work lays the foundation for building near-real-time (NRT) short-term SEP event predictions using robust ML methods.