  The solar X-ray irradiance is significantly heightened during the course of a
solar flare, which can cause radio blackouts due to ionization of the atoms in
the ionosphere. As the duration of a solar flare is not related to the size of
that flare, it is not directly clear how long those blackouts can persist.
Using a random forest regression model trained on data taken from X-ray light
curves, we have developed a direct forecasting method that predicts how long
the event will remain above background levels. We test this on a large
collection of flares observed with GOES-15, and show that it generally
outperforms simple linear regression. This forecast is computationally light
enough to be performed in real time, allowing for the prediction to be made
during the course of a flare.
