Short time series of website visits prediction by RBF neural networks and support vector machine regression

Authors:
Vladimir Olej;Jana Filipova
Affiliations:
Institute of System Engineering and Informatics, Faculty of Economics and Administration, University of Pardubice, Pardubice, Czech Republic;Institute of System Engineering and Informatics, Faculty of Economics and Administration, University of Pardubice, Pardubice, Czech Republic
Venue:
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Year:
2012

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Abstract

The paper presents basic notions of web mining, radial basis function (RBF) neural networks and ε -insensitive support vector machine regression (ε -SVR) for the prediction of a short time series (website of the University of Pardubice, Czech Republic). There are various short time series according to different visitors or interest of visitors (students, employees, documents). Further, a model (including RBF neural networks and ε -SVRs) was developed for short time series prediction. The model includes decomposition of data to training and testing data set using the cluster procedure. The next part of the paper describes the predictions of the web domain visits, which depend on this model, as well as outlines an analysis of the results.