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Abstract Solar energetic particle (SEP) events pose significant risks to both space and ground-level infrastructure, as well as to human health in space. Understanding and predicting these events are critical for mitigating their potential impacts. In this paper, we address the challenge of predicting SEP events using proton flux data. We leverage some of the most recent advances in time series data mining, such as shapelets and the matrix profile, to propose a simple and easily understandable prediction approach. Our objective is to mitigate the interpretability challenges inherent to most machine learning models and to show that other methods exist that can not only yield accurate forecasts but also facilitate exploration and insight generation within the data domain. For this purpose, we construct a multivariate time series data set consisting of proton flux data recorded by the National Oceanic and Atmospheric Administration's geosynchronous orbit Earth-observing satellite. Then, we use our proposed approach to mine shapelets and make predictions using a random forest classifier. We demonstrate that our approach rivals state-of-the-art SEP prediction, offering superior interpretability and the ability to predict SEP events before their parent eruptive flares.