Internet traffic prediction is an important task for many applications, such as adaptive applications, congestion control, admission control, anomaly detection and bandwidth allocation. In addition, efficient methods of resource management can be used to gain performance and reduce costs. The popularity of the newest deep learning methods has been increasing in several areas, but there is a lack of studies concerning time series prediction. This paper compares two different artificial neural network approaches for the Internet traffic forecast. One is a Multilayer Perceptron (MLP) and the other is a deep learning Stacked Autoencoder (SAE). It is shown herein how a simpler neural network model, such as the MLP, can work even better than a more complex model, such as the SAE, for Internet traffic prediction. © 2014 IFIP International Federation for Information Processing.
CITATION STYLE
Oliveira, T. P., Barbar, J. S., & Soares, A. S. (2014). Multilayer perceptron and stacked autoencoder for Internet traffic prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8707 LNCS, pp. 61–71). Springer Verlag. https://doi.org/10.1007/978-3-662-44917-2_6
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