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
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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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
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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
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