  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.
