  There is a lack of research on the analysis of per-user traffic in cellular
networks, for deriving and following traffic-aware network management.
\textcolor{black}{In fact, the legacy design approach, in which resource
provisioning and operation control are performed based on the cell-aggregated
traffic scenarios, are not so energy- and cost-efficient and need to be
substituted with user-centric predictive analysis of mobile network traffic and
proactive network resource management.} Here, we shed light on this problem by
designing traffic prediction tools that utilize standard machine learning (ML)
tools, including long short-term memory (LSTM) and autoregressive integrated
moving average (ARIMA) on top of per-user data. We present an expansive
empirical evaluation of the designed solutions over a real network traffic
dataset. Within this analysis, the impact of different parameters, such as the
time granularity, the length of future predictions, and feature selection are
investigated. As a potential application of these solutions, we present an
ML-powered Discontinuous reception (DRX) scheme for energy saving. Towards this
end, we leverage the derived ML models for dynamic DRX parameter adaptation to
user traffic. The performance evaluation results demonstrate the superiority of
LSTM over ARIMA in general, especially when the length of the training time
series is high enough, and it is augmented by a \textit{wisely}-selected set of
features. Furthermore, the results show that adaptation of DRX parameters by
online prediction of future traffic provides much more energy-saving at low
latency cost in comparison with the legacy cell-wide DRX parameter adaptation.
