Voice and data traffic modeling and prediction for a third generation mobile network using machine learning methods

The purpose of the thesis is to derive models for traffic characteristics of a 3G network which is commercially deployed in Turkey and predict voice and data traffic by using various machine learning methods. The machine learning methods which were employed are Support Vector Machines (SVM), Multilayer Perceptron (MLP), Random Forest (RF) and Radial Basis Function Neural Network (RBF). Additionally, the Holt-Winters method has been applied to develop prediction models as a statistical method. Four different type of UMTS network traffic data have been utilized in order to build traffic prediction models. The performance of the forecasting models for the data sets has been assessed using Mean Absolute Percentage Error (MAPE). Finally, the performance of statistical and machine learning regression methods have been compared and the results show that SVM and Holt-Winters based models usually perform better than the ones obtained by the other methods.