Large fraction of studies of active galactic nuclei objects is based on performing follow-up observations using high-sensitivity instruments of high flux states observed by monitoring instruments (the so-called Target of Opportunity, ToO). Due to transient nature of such enhanced states it is essential to quickly evaluate if such a ToO event should be followed. We use a machine learning method to assess the possibility to predict the evolution of high flux states in gamma-ray band observed with Fermi-LAT in context of following such alerts with current and future Cherenkov telescopes. We probe flux and Test Statistic predictions using different training schemes and sample selections. We conclude that a partial prediction of the flux over a time scale of one day with an accuracy of $\sim 35\text{\%}$ is possible. The method provides accurate predictions of the raising/falling emission trend with 60–75% probability, however deeper investigations shows that this is likely based on typical properties of the source, rather than on the result of most recent measurements.

对活动星系核天体的大部分研究是基于使用监测仪器（所谓的机会目标，ToO）观测到的高通量状态的高灵敏度仪器进行后续观测。由于此类增强状态的瞬态性质，必须快速评估是否应遵循此类 ToO 事件。我们使用机器学习方法来评估预测费米-LAT观测到的伽马射线带中高通量状态演变的可能性，并在当前和未来的切伦科夫望远镜跟踪此类警报的情况下进行。我们使用不同的训练方案和样本选择来探测通量和测试统计预测。我们得出的结论是，在一天的时间尺度内对通量的部分预测精度为$～35\text{\%}$是可能的。该方法以 60-75% 的概率提供了上升/下降排放趋势的准确预测，但更深入的调查表明，这可能基于源的典型特性，而不是最近的测量结果。