  Mass events represent one of the most challenging scenarios for mobile
networks because, although their date and time are usually known in advance,
the actual demand for resources is difficult to predict due to its dependency
on many different factors. Based on data provided by a major European carrier
during mass events in a football stadium comprising up to 30.000 people, 16
base station sectors and $1$Km$^2$ area, we performed a data-driven analysis of
the radio access network infrastructure dynamics during such events. Given the
insights obtained from the analysis, we developed ARENA, a model-free deep
learning Radio Access Network (RAN) capacity forecasting solution that, taking
as input past network monitoring data and events context information, provides
guidance to mobile operators on the expected RAN capacity needed during a
future event. Our results, validated against real events contained in the
dataset, illustrate the effectiveness of our proposed solution.
