  Mobile network traffic prediction is an important input in to network
capacity planning and optimization. Existing approaches may lack the speed and
computational complexity to account for bursting, non-linear patterns or other
important correlations in time series mobile network data. We compare the
performance of two deep learning architectures - Long Short-Term Memory (LSTM)
and Gated Recurrent Unit (GRU) - for predicting mobile Internet traffic using
two months of Telecom Italia data for the metropolitan area of Milan. K-Means
clustering was used a priori to group cells based on Internet activity and the
Grid Search method was used to identify the best configurations for each model.
The predictive quality of the models was evaluated using root mean squared
error. Both Deep Learning algorithms were effective in modeling Internet
activity and seasonality, both within days and across two months. We find
variations in performance across clusters within the city. Overall, the LSTM
outperformed the GRU in our experiments.
