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TitleInternet traffic forecasting using neural networks
Author(s)Rocha, Miguel
Sousa, Pedro
Cortez, Paulo
Rio, Miguel
KeywordsArtificial intelligence
Computer communications
Issue dateJul-2006
JournalIEEE International Joint Conference on Neural Networks (IJCNN)
CitationINTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, Vancouver, BC, Canada, 2006 – “IJCNN 2006 : proceedings of the International Joint Conference on Neural Networks”. [S.l.] : IEEE, 2006. ISBN 0-7803-9490-9. p. 4942-4949.
Abstract(s)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).
TypeConference paper
AccessOpen access
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings
DI/CCTC - Artigos (papers)

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