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

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F12%3A39894570" target="_blank" >RIV/00216275:25410/12:39894570 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1007/978-3-642-29347-4_16" target="_blank" >http://dx.doi.org/10.1007/978-3-642-29347-4_16</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-642-29347-4_16" target="_blank" >10.1007/978-3-642-29347-4_16</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

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

  • Original language description

    The paper presents basic notions of web mining, radial basis function (RBF) neural networks and epsilon-insensitive support vector machine regression (epsilon-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 epsilon-SVRs) was developed for short time series prediction. Themodel 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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2012

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Artificial Intelligence and Soft Computing

  • ISBN

    978-3-642-29346-7

  • ISSN

    0302-9743

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    135-142

  • Publisher name

    Springer

  • Place of publication

    Berlin

  • Event location

    Zakopane

  • Event date

    Apr 29, 2012

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article