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Solar flare forecasting can be realized by means of the analysis of magneticdata through artificial intelligence techniques. The aim is to predict whethera magnetic active region (AR) will originate solar flares above a certain classwithin a certain amount of time. A crucial issue is concerned with the way theadopted machine learning method is implemented, since forecasting resultsstrongly depend on the criterion with which training, validation, and test setsare populated. In this paper we propose a general paradigm to generate thesesets in such a way that they are independent from each other and internallywell-balanced in terms of AR flaring effectiveness. This set generation processprovides a ground for comparison for the performance assessment of machinelearning algorithms. Finally, we use this implementation paradigm in the caseof a deep neural network, which takes as input videos of magnetograms recordedby the Helioseismic and Magnetic Imager on-board the Solar Dynamics Observatory(SDO/HMI). To our knowledge, this is the first time that the solar flareforecasting problem is addressed by means of a deep neural network for videoclassification, which does not require any a priori extraction of features fromthe HMI magnetograms.

Implementation paradigm for supervised flare forecasting studies: a deep learning application with video data