Prediction of Oil Prices Using Bagging and Random Subspace

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86092535" target="_blank" >RIV/61989100:27240/14:86092535 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/14:86092535
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-08156-4_34" target="_blank" >http://dx.doi.org/10.1007/978-3-319-08156-4_34</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-08156-4_34" target="_blank" >10.1007/978-3-319-08156-4_34</a>

Result language
angličtina
Original language name
Prediction of Oil Prices Using Bagging and Random Subspace
Original language description
The problem of predicting oil prices is worthy of attention. As oil represents the backbone of the world economy, the goal of this paper is to design a model, which is more accurate. We modeled the prediction process comprising of three steps: feature selection, data partitioning and analyzing the prediction models. Six prediction models namely: Multi-Layered Perceptron (MLP), Sequential Minimal Optimization for regression (SMOreg), Isotonic Regression, Multilayer Perceptron Regressor (MLP Regressor), Extra-Tree and Reduced Error Pruning Tree (REPtree). These prediction models were selected and tested after experimenting with other several most widely used prediction models. The comparison of these six algorithms with previous work is presented based on Root mean squared error (RMSE) to find out the best suitable algorithm. Further, two meta schemes namely Bagging and Random subspace are adopted and compared with previous algorithms using Mean squared error (MSE) to evaluate performance. Experimental evidence illustrate that the random subspace scheme outperforms most of the existing techniques. Springer International Publishing Switzerland 2014.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2014
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 303
ISBN
978-3-319-08155-7
ISSN
2194-5357
e-ISSN
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Number of pages
12
Pages from-to
343-354
Publisher name
Springer-Verlag Berlin Heidelberg
Place of publication
Berlin Heidelberg
Event location
Ostrava
Event date
Jun 23, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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