  The EU funded the FLARECAST project, that ran from Jan 2015 until Feb 2018.
FLARECAST had a R2O focus, and introduced several innovations into the
discipline of solar flare forecasting. FLARECAST innovations were: first, the
treatment of hundreds of physical properties viewed as promising flare
predictors on equal footing, extending multiple previous works; second, the use
of fourteen (14) different ML techniques, also on equal footing, to optimize
the immense Big Data parameter space created by these many predictors; third,
the establishment of a robust, three-pronged communication effort oriented
toward policy makers, space-weather stakeholders and the wider public.
FLARECAST pledged to make all its data, codes and infrastructure openly
available worldwide. The combined use of 170+ properties (a total of 209
predictors are now available) in multiple ML algorithms, some of which were
designed exclusively for the project, gave rise to changing sets of
best-performing predictors for the forecasting of different flaring levels. At
the same time, FLARECAST reaffirmed the importance of rigorous training and
testing practices to avoid overly optimistic pre-operational prediction
performance. In addition, the project has (a) tested new and revisited
physically intuitive flare predictors and (b) provided meaningful clues toward
the transition from flares to eruptive flares, namely, events associated with
coronal mass ejections (CMEs). These leads, along with the FLARECAST data,
algorithms and infrastructure, could help facilitate integrated space-weather
forecasting efforts that take steps to avoid effort duplication. In spite of
being one of the most intensive and systematic flare forecasting efforts
to-date, FLARECAST has not managed to convincingly lift the barrier of
stochasticity in solar flare occurrence and forecasting: solar flare prediction
thus remains inherently probabilistic.
