Multivariate Modeling and Analysis for Cellular Traffic Prediction Using Call Detail Records

Abstract

Data traffic prediction is essential for resource planning and allocation for service providers. Call Detail Records (CDR) provides invaluable information about user movements and behavior. However, the scale and complexity of CDR arise problems with its continuous usage in real-life issues. In this study, we propose a summary data structure out of CDR data to improve analysis performance. We then use this new data structure to make inferences using Multivariate Time Series analyses about the data traffic. We used several models, including Long Short-Term Memory networks (LSTM) and eXtreme Gradient Boosting (XGBoost), to verify the effectiveness of this approach. According to the results, our multivariate approach ensures usage trend capture. The research findings are efficient and suitable for predicting real-world network traffic based on usage type.