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Short Time Series of Website Visits Prediction by RBF Neural Networks and Support Vector Machine Regression

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7267)

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.

Keywords

  • Web mining
  • RBF neural networks
  • ε-SVR
  • short time series
  • data set decomposition
  • prediction

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© 2012 Springer-Verlag Berlin Heidelberg

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Olej, V., Filipova, J. (2012). Short Time Series of Website Visits Prediction by RBF Neural Networks and Support Vector Machine Regression. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_16

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  • DOI: https://doi.org/10.1007/978-3-642-29347-4_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29346-7

  • Online ISBN: 978-3-642-29347-4

  • eBook Packages: Computer ScienceComputer Science (R0)