Severe space weather produced by disturbed conditions on the Sun results
in harmful effects both for humans in space and in high-latitude
commercial flights, and for technological systems such as spacecraft or
communications. Also, geomagnetically induced currents flowing on long
ground-based conductors, such as power networks or pipelines,
potentially threaten critical infrastructures on Earth. The first step
in developing an alarm system against geomagnetically induced currents
is to forecast them. This is a challenging task, though, given the
highly non-linear dependencies of the response of the magnetosphere to
these perturbations. In the last few years, modern machine-learning
models have shown to be very good at predicting magnetic activity
indices as the SYM-H. However, such complex models are on the one hand
difficult to tune, and on the other hand they are known to bring along
potentially large prediction uncertainties which are generally difficult
to estimate. In this work we aim at predicting the SYM-H index
characterising geomagnetic storms one hour in advance, using public
interplanetary magnetic field data from the Sun--Earth L1 Lagrange point
and SYM-H. We implement a type of machine-learning model called long
short-term memory networks. Our scope is to estimate -for the first time
to our knowledge- the prediction uncertainties coming from a
deep-learning model in the context of space weather. The resulting
uncertainties turn out to be sizeable at the critical stages of the
geomagnetic storms. Our methodology includes as well an efficient
optimisation of important hyper-parameters of the long short-term memory
network and robustness tests.