  Network slicing is increasingly used to partition network infrastructure
between different mobile services. Precise service-wise mobile traffic
forecasting becomes essential in this context, as mobile operators seek to
pre-allocate resources to each slice in advance, to meet the distinct
requirements of individual services. This paper attacks the problem of
multi-service mobile traffic forecasting using a sequence-to-sequence (S2S)
learning paradigm and convolutional long short-term memories (ConvLSTMs). The
proposed architecture is designed so as to effectively extract complex
spatiotemporal features of mobile network traffic and predict with high
accuracy the future demands for individual services at city scale. We conduct
experiments on a mobile traffic dataset collected in a large European
metropolis, demonstrating that the proposed S2S-ConvLSTM can forecast the
mobile traffic volume produced by tens of different services in advance of up
to one hour, by just using measurements taken during the past hour. In
particular, our solution achieves mean absolute errors (MAE) at antenna level
that are below 13KBps, outperforming other deep learning approaches by up to
31.2%.
