Forecasting Network Capacity for Global Enterprise Backbone Networks using Machine Learning Techniques

This paper presents a machine learning approach to forecasting network capacity for global enterprise-level backbone networks. By leveraging historical traffic data, we develop a predictive model that accurately forecasts future demands. The effectiveness of our approach is validated through rigorous testing against established benchmarks, demonstrating significant improvements in forecasting accuracy.