Ensemble neurocomputing based oil price prediction

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86097019" target="_blank" >RIV/61989100:27240/15:86097019 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-13572-4_24" target="_blank" >http://dx.doi.org/10.1007/978-3-319-13572-4_24</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-13572-4_24" target="_blank" >10.1007/978-3-319-13572-4_24</a>

Result language
angličtina
Original language name
Ensemble neurocomputing based oil price prediction
Original language description
In this paper, we investigated an ensemble neural network for the prediction of oil prices. Daily data from 1999 to 2012 were used to predict the West Taxes, Intermediate. Data were separated into four phases of training and testing using different percentages and obtained seven sub-datasets after implementing different attribute selection algorithms. We used three types of neural networks: Feed forward, Recurrent and Radial Basis Function networks. Finally a good ensemble neural network model is formulated by the weighted average method. Empirical results illustrated that the ensemble neural network outperformed other models. (C) Springer International Publishing Switzerland 2015.
Czech name
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Czech description
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Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Advances in Intelligent Systems and Computing. Volume 334
ISBN
978-3-319-13571-7
ISSN
2194-5357
e-ISSN
—
Number of pages
10
Pages from-to
293-302
Publisher name
Springer
Place of publication
Heidelberg
Event location
Addis Ababa
Event date
Nov 17, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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