  Traffic prediction plays a vital role in efficient planning and usage of
network resources in wireless networks. While traffic prediction in wired
networks is an established field, there is a lack of research on the analysis
of traffic in cellular networks, especially in a content-blind manner at the
user level. Here, we shed light into this problem by designing traffic
prediction tools that employ either statistical, rule-based, or deep machine
learning methods. First, we present an extensive experimental evaluation of the
designed tools over a real traffic dataset. Within this analysis, the impact of
different parameters, such as length of prediction, feature set used in
analyses, and granularity of data, on accuracy of prediction are investigated.
Second, regarding the coupling observed between behavior of traffic and its
generating application, we extend our analysis to the blind classification of
applications generating the traffic based on the statistics of traffic
arrival/departure. The results demonstrate presence of a threshold number of
previous observations, beyond which, deep machine learning can outperform
linear statistical learning, and before which, statistical learning outperforms
deep learning approaches. Further analysis of this threshold value represents a
strong coupling between this threshold, the length of future prediction, and
the feature set in use. Finally, through a case study, we present how the
experienced delay could be decreased by traffic arrival prediction.
