Space weather encompasses the Solar-Terrestrial environment’s interactions, emphasizing phenomena in the solar environment, such as sunspots, coronal mass ejections, and solar flares. The latter is one of the most relevant solar activity phenomena and the study object in this paper. The main challenge involved is to monitor the occurrence of solar flares and identifying features that help predict this phenomenon for specific classes. When M- or X-class flares occur, they may impact the health of astronauts and services used daily, such as satellite positioning services, telecommunications, and electrical networks — systems essential for modern life. This paper evaluates three methods for solar flares automatic classification: Support Vector Machine, Random Forest, and Light Gradient Boosting Machine. In addition to predicting the three available methods, we highlight the importance of variables for the LightGBM and Random Forest methods and the study of methods for data balancing. We also propose a system for predicting M- and X-classes flares 24, 48, and 72  h in advance. The methodologies used in the estimation and model validation processes involved cross-validation with stratified sampling and holdout methods, considering the use of balanced and imbalanced data. For the 24  h prediction horizon, we obtain the True Skill Statistic equal to 0.58 combining the algorithms via majority vote. We also achieved true positive and true negative rates equal to 0.81 and 0.77, respectively. For the forecasting in the interval between 24  h and 48  h, we obtained TSS higher than 0.50. In turn, for the interval of 48  h to 72  h, we obtained TSS higher than 0.54.

太空天气涵盖了太阳和地球环境的相互作用，强调了太阳环境中的现象，例如黑子，日冕物质抛射和太阳耀斑。后者是最相关的太阳活动现象之一，也是本文的研究对象。涉及的主要挑战是监视太阳耀斑的发生并确定有助于预测特定类别这种现象的特征。当发生M级或X级耀斑时，它们可能会影响宇航员和日常使用的服务（例如卫星定位服务，电信和电气网络等现代生活必不可少的系统）的健康。本文评估了太阳耀斑自动分类的三种方法：支持向量机，随机森林和光梯度增强机。除了预测三种可用的方法外，我们还强调了变量对于LightGBM和Random Forest方法以及数据平衡方法的研究的重要性。我们还提出了一种用于预测M级和X级耀斑24、48和72的系统  提前h。考虑到平衡和不平衡数据的使用，估计和模型验证过程中使用的方法涉及使用分层采样和保留方法进行交叉验证。对于24  小时的预测范围，我们通过多数表决将算法组合在一起，得出的真实技能统计信息等于0.58。我们还分别获得了分别为0.81和0.77的真实阳性率和真实阴性率。对于在24   h和48   h之间的时间间隔的预测，我们获得的TSS高于0.50。反过来，对于48   h至72   h的时间间隔，我们获得的TSS高于0.54。