Web traffic prediction with artificial neural networks

Adam Gluszek; Michal Kekez; Filip Rudzinski

doi:10.1117/12.610751

23 February 2005 Web traffic prediction with artificial neural networks

Adam Gluszek, Michal Kekez, Filip Rudzinski

Proceedings Volume 5775, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments III; (2005) https://doi.org/10.1117/12.610751
Event: Wilga 2004, 2004, Wilga, Poland

Abstract

The main aim of the paper is to present application of the artificial neural network in the web traffic prediction. First, the general problem of time series modelling and forecasting is shortly described. Next, the details of building of dynamic processes models with the neural networks are discussed. At this point determination of the model structure in terms of its inputs and outputs is the most important question because this structure is a rough approximation of the dynamics of the modelled process. The following section of the paper presents the results obtained applying artificial neural network (classical multilayer perceptron trained with backpropagation algorithm) to the real-world web traffic prediction. Finally, we discuss the results, describe weak points of presented method and propose some alternative approaches.

Citation Download Citation

Adam Gluszek, Michal Kekez, and Filip Rudzinski "Web traffic prediction with artificial neural networks", Proc. SPIE 5775, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments III, (23 February 2005); https://doi.org/10.1117/12.610751

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