Kapil Patil¹ and Bhavin Desai², ¹Principal Technical Program Manager, USA, ²Product Manager, USA

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

Network capacity forecasting, Machine learning,Time series forecasting, ARIMA, Predictive modelling & Capacity Planning