This paper presents a framework based on the data analysis concept to automate the management of resources in cellular networks. Three processes are defined: identifying and detecting anomalies, analyzing the causes, and triggering adequate recovery actions. First, the proposed solution executes Deep Learning algorithms to forecast the normal behavior of the network and defines dynamic thresholds. Then, it identifies cells with peak demands and raises alarms if the measured real-time data exceeds the threshold values. Second, we define QoS optimization methods to proceed with suitable design for resource allocation as well as fault detection and avoidance. Hence, we distinguish three cases and define two classes of data: Real-time and non-real-time traffic. This solution is applied to a pre-analyzed semi-synthetic real dataset extracted from the CDRs (Call Detail Records) in Milan city, Italy. This dataset contains the Internet activity records of two months in three areas. The preliminary results elucidate the feasibility and preeminence of our proposed anomaly detection framework.