Revolutionizing Internet Traffic Forecasting in the UAE with Machine Learning

At least 66% of the global population is connected to the internet, mainly due to high internet penetration rates in most countries like the United Arab Emirates (UAE). The growing number of internet users is expected to continue. However, deriving accurate forecasts using traditional traffic scheduling and path planning approaches is inherently challenging. This issue is particularly detrimental since timely and accurate internet traffic flow predictions are crucial for network performance improve-ments. This paper illustrates the feasibility of using machine learning (ML) methods for traffic flow and demand prediction. Specifically, the research indicates the ability of ML to rapidly forecast internet use in the UAE using real-time and historical data.