Prediction of Oil Prices Using Bagging and Random Subspace

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61989100:27740/14:86092535

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Prediction of Oil Prices Using Bagging and Random Subspace

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Prediction of Oil Prices Using Bagging and Random Subspace

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 303

ISBN

978-3-319-08155-7

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

12

Strana od-do

343-354

Název nakladatele

Springer-Verlag Berlin Heidelberg

Místo vydání

Berlin Heidelberg

Místo konání akce

Ostrava

Datum konání akce

23. 6. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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