  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 ~35% 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.
