New models for long-term Internet traffic forecasting using artificial neural networks and flow based information

This paper investigates the use of ensembles of artificial neural networks in predicting long-term Internet traffic. It discusses a method for collecting traffic information based on flows, obtained with the NetFlow protocol, to build the time series. It also proposes four traffic forecasting models based on ensembles of TLFNs (Time-Lagged FeedFoward Networks), each one differing from the others by the way it reads the training data and by the number of artificial neural networks used in the forecasts. The proposed prediction models are confronted with the classic method of Holt-Winters, by comparing the mean absolute percentage error (MAPE) of the forecasts. It is concluded that the proposed models perform well, and can be considered a good option for planning network links that transport Internet traffic.