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.

本文提出一种专门用于精确预测网络范围内移动数据流量的时空神经网络（STN）结构和双 STN 技术（D-STN），通过在城市和农村地区 60 天的真实移动流量数据集上进行的实验，证明了所提出的（D-）STN 方法可以实现高达 10 小时的精确预测，并且与其他流行的预测方法相比，可以获得高达 61% 的更小预测误差。