Multiscale Internet traffic forecasting using neural networks and time series methods

Abstract

This article presents three methods to forecast accurately the amount of traffic in TCP=IP based networks: a novel neural network ensemble approach and two important adapted time series methods (ARIMA and Holt-Winters). In order to assess their accuracy, several experiments were held using real-world data from two large Internet service providers. In addition, different time scales (5min, 1h and 1 day) and distinct forecasting lookaheads were analysed. The experiments with the neural ensemble achieved the best results for 5 min and hourly data, while the Holt-Winters is the best option for the daily forecasts. This research opens possibilities for the development of more efficient traffic engineering and anomaly detection tools, which will result in financial gains from better network resource management.