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Machine learning is nowadays the methodology of choice for flare forecastingand supervised techniques, in both their traditional and deep versions, arebecoming the most frequently used ones for prediction in this area of spaceweather. Yet, machine learning has not been able so far to realize an operatingwarning system for flaring storms and the scientific literature of the lastdecade suggests that its performances in the prediction of intense solar flaresare not optimal.  The main difficulties related to forecasting solar flaring storms areprobably two. First, most methods are conceived to provide probabilisticpredictions and not to send binary yes/no indications on the consecutiveoccurrence of flares along an extended time range. Second, flaring storms aretypically characterized by the explosion of high energy events, which areseldom recorded in the databases of space missions; as a consequence,supervised methods are trained on very imbalanced historical sets, which makesthem particularly ineffective for the forecasting of intense flares.  Yet, in this study we show that supervised machine learning could be utilizedin a way to send timely warnings about the most violent and most unexpectedflaring event of the last decade, and even to predict with some accuracy theenergy budget daily released by magnetic reconnection during the whole timecourse of the storm. Further, we show that the combination ofsparsity-enhancing machine learning and feature ranking could allow theidentification of the prominent role that energy played as an Active Regionproperty in the forecasting process.

Machine learning as a flaring storm warning machine: Was a warning machine for the September 2017 solar flaring storm possible?