  The ionosphere electromagnetic activity is a major factor of the quality of
satellite telecommunications, Global Navigation Satellite Systems (GNSS) and
other vital space applications. Being able to forecast globally the Total
Electron Content (TEC) would enable a better anticipation of potential
performance degradations. A few studies have proposed models able to predict
the TEC locally, but not worldwide for most of them. Thanks to a large record
of past TEC maps publicly available, we propose a method based on Deep Neural
Networks (DNN) to forecast a sequence of global TEC maps consecutive to an
input sequence of TEC maps, without introducing any prior knowledge other than
Earth rotation periodicity. By combining several state-of-the-art
architectures, the proposed approach is competitive with previous works on TEC
forecasting while predicting the TEC globally.
