Internet Traffic Forecasting using Neural Networks

Paulo Cortez, Miguel Rio, Miguel Rocha, Pedro Sousa

Universidade do Minho
Departamento de Informática
P-4710-057 Braga, Portugal

Tel.: +351 253 604430
Fax.: +351 253 604471
E-mail: pns (at)


The forecast of Internet traffic is an important issue that has received few attention from the computer networks field. By improving this task, efficient traffic engineering and anomaly detection tools can be created, resulting in economic gains from better resource management. This paper presents a Neural Network Ensemble (NNE) for the prediction of TCP/IP traffic using a Time Series Forecasting (TSF) point of view. Several experiments were devised by considering real-world data from two large Internet Service Providers. In addition, different time scales (e.g. every five minutes and hourly) and forecasting horizons were analyzed. Overall, the NNE approach is competitive when compared with other TSF methods (e.g. Holt-Winters and ARIMA).

Proc of 2006 International Joint Conference on Neural Networks, pp 4942-4949, IEEE, Vancouver, Canada, Jul 16-21, 2006