  Prediction of user traffic in cellular networks has attracted profound
attention for improving resource utilization. In this paper, we study the
problem of network traffic traffic prediction and classification by employing
standard machine learning and statistical learning time series prediction
methods, including long short-term memory (LSTM) and autoregressive integrated
moving average (ARIMA), respectively. We present an extensive experimental
evaluation of the designed tools over a real network traffic dataset. Within
this analysis, we explore the impact of different parameters to the
effectiveness of the predictions. We further extend our analysis to the problem
of network traffic classification and prediction of traffic bursts. The
results, on the one hand, demonstrate superior performance of LSTM over ARIMA
in general, especially when the length of the training time series is high
enough, and it is augmented by a wisely-selected set of features. On the other
hand, the results shed light on the circumstances in which, ARIMA performs
close to the optimal with lower complexity.
