Mobile Network Traffic Classification and Prediction

Abstract

Call Detail Records (CDRs) from mobile networks offer rich insights into network performance and user behavior. In this study, we analyze CDR data from Telecom Italia, encompassing spatiotemporal patterns across Milan, segmented into a 100x100 grid with each cell covering 0.3 kilometers. By analyzing the spatiotemporal dynamics of CDR data, we classify the network traffic into four categories: highest, high, moderate and low with high network traffic regions predominantly located in the city center. After network traffic classification, we predict future traffic patterns. We utilize automated machine learning (AutoML) tools and the state-of-the-art TimeGPT model for network traffic forecasting. Comparative analysis reveals that AUTOML performs better then TIMEGPT, delivering superior prediction and performing better on the various evaluation metrices resultantly capturing complex temporal and spatial relationships in the data. These predictive capabilities enable dynamic resource allocation, enhanced congestion management and improved network efficiency. Our findings underscore the potential of both AUTOML and TimeGPT, to some extent AUTOML appears to be more scalable and adaptable solution for mobile network traffic classification and forecasting, marking a significant advancement in network planning and optimization for urban environments