  Forecasting with high accuracy the volume of data traffic that mobile users
will consume is becoming increasingly important for precision traffic
engineering, demand-aware network resource allocation, as well as public
transportation. Measurements collection in dense urban deployments is however
complex and expensive, and the post-processing required to make predictions is
highly non-trivial, given the intricate spatio-temporal variability of mobile
traffic due to user mobility. To overcome these challenges, in this paper we
harness the exceptional feature extraction abilities of deep learning and
propose a Spatio-Temporal neural Network (STN) architecture purposely designed
for precise network-wide mobile traffic forecasting. We present a mechanism
that fine tunes the STN and enables its operation with only limited ground
truth observations. We then introduce a Double STN technique (D-STN), which
uniquely combines the STN predictions with historical statistics, thereby
making faithful long-term mobile traffic projections. Experiments we conduct
with real-world mobile traffic data sets, collected over 60 days in both urban
and rural areas, demonstrate that the proposed (D-)STN schemes perform up to
10-hour long predictions with remarkable accuracy, irrespective of the time of
day when they are triggered. Specifically, our solutions achieve up to 61%
smaller prediction errors as compared to widely used forecasting approaches,
while operating with up to 600 times shorter measurement intervals.
