Abstract Solar flares are large explosions in the sun’s atmosphere. They can damage satellites and overload electrical systems. To manage that risk, finding methods of efficiently predicting future events is very important. In this paper we introduce a full-Sun flare prediction method based on the Hidden Markov modelling with two hidden states. We concentrate on the soft X-ray emission data near the minimum of solar cycle and consider two different driving dynamics for both states, namely the independent identically distributed (IID) random variables and the autoregressive (AR) processes, the latter introducing a memory structure. We compare prediction performance for the IID and AR approaches and also with a naive prediction equal to the last observation. The solar X-flux dynamics is predicted by using the day-ahead forecasts. We calculate point and interval forecasts and perform relevant statistical tests to choose the best method. It appears that the AR approach is clearly superior to the IID and naive both by means of point and interval forecasts. Moreover, it can well detect the higher state which can lead to very strong energy releases. Significant development of the model would be necessary to forecast solar flares over an entire solar cycle.

中文翻译：

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隐马尔可夫模型对太阳耀斑的预测性能

摘要 太阳耀斑是太阳大气中的大爆炸。它们会损坏卫星并使电力系统过载。为了管理这种风险，找到有效预测未来事件的方法非常重要。在本文中，我们介绍了一种基于具有两个隐藏状态的隐马尔可夫模型的全太阳耀斑预测方法。我们专注于太阳周期最小值附近的软 X 射线发射数据，并考虑两种状态的两种不同驱动动力学，即独立同分布 (IID) 随机变量和自回归 (AR) 过程，后者引入了记忆结构. 我们比较了 IID 和 AR 方法的预测性能，也比较了与上次观察结果相同的幼稚预测。使用日前预测来预测太阳 X 通量动力学。我们计算点和区间预测并执行相关统计测试以选择最佳方法。通过点预测和区间预测，AR 方法似乎明显优于 IID 和朴素。此外，它可以很好地检测到可能导致非常强烈的能量释放的更高状态。该模型的重大发展对于预测整个太阳周期的太阳耀斑是必要的。