Mobile User-Activity Prediction Utilizing LSTM Recurrent Neural Network

Abstract

The demand for mobile services is increasing exponentially and mobile telecommunication operators are facing new challenges such as bandwidth choking and resource allocation issues consequently. One of the most promising methods for overcoming these challenges is using Big Data Analytics on Call Detail Record (CDR). In this paper, a Long-Short Term Memory neural network is utilized for user activity prediction for the first time. The real CDR data is preprocessed and used for training the neural network. The accuracy of this prediction is evaluated in normal values and anomalies. The proposed system can predict user activity accurately using a limited amount of training data and its performance in anomalous behaviors is promising. The networks ability to be generalized is also evaluated with the cross-dataset test. The neural network is trained with one cells data and predict another cells activity properly. The proposed system is a step forward toward designing a practical and efficient CDR anomaly prediction system.