An all-clear flare prediction is a type of solar flare forecasting that puts more emphasis on predicting non-flaring instances (often relatively small flares and flare quiet regions) with high precision while still maintaining valuable predictive results. While many flare prediction studies do not address this problem directly, all-clear predictions can be useful in operational context. However, in all-clear predictions, finding the right balance between avoiding false negatives (misses) and reducing the false positives (false alarms) is often challenging. Our study focuses on training and testing a set of interval-based time series classifiers named Time Series Forest (TSF). These classifiers will be used towards building an all-clear flare prediction system by utilizing multivariate time series data. Throughout this paper, we demonstrate our data collection, predictive model building and evaluation processes, and compare our time series classification models with baselines using our benchmark datasets. Our results show that time series classifiers provide better forecasting results in terms of skill scores, precision and recall metrics, and they can be further improved for more precise all-clear forecasts by tuning model hyperparameters.

翻译：一种非常清楚的耀斑预测是一种非常清楚的太阳耀斑预测,它更加强调预测非发光事件(通常是相对较小的耀斑和耀斑安静地区),同时仍然保持宝贵的预测结果。虽然许多耀斑预测研究并不直接解决这个问题,但完全清楚的预测在实际操作中是有用的。然而,在全清晰的预测中,在避免假负数(失误)和减少假正数(假警报)之间找到适当的平衡往往具有挑战性。我们的研究侧重于培训和测试一套以间隔为基础的时间序列分类器,名为《时间系列森林》。这些分类器将被用于利用多变时间序列数据建立一个完全清楚的耀斑预测系统。我们在整个文件中展示了我们的数据收集、预测模型建设和评价过程,并将我们的时间序列模型与基准数据集的基线进行比较。我们的结果显示,时间序列分类器在技能分数、精确度和回顾度方面提供了更好的预报结果,并且可以通过调整模型超参数来进一步改进所有更精确的精确的预报。