Comparison of hybrid intelligent approaches for prediction of crude oil price

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86097021" target="_blank" >RIV/61989100:27240/15:86097021 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of hybrid intelligent approaches for prediction of crude oil price

Popis výsledku v původním jazyce

Crude oil price prediction is a challenging task due to its complex nonlinear and chaotic behavior. There is a great need for oil price volatility measuring and modeling of oil price chaotic behavior. During the last couple of decades, both academiciansand practitioners have devoted proactive knowledge to address this issue. Combined predictors are one of the most promising forms in Machine learning (ML). It can be found in different styles in the literature such as Meta learning, Ensemble based prediction, Hybrid methods and more. The aim of this paper to conduct comprehensive comparisons among the combined prediction model in order to improve the performance.

Název v anglickém jazyce

Comparison of hybrid intelligent approaches for prediction of crude oil price

Popis výsledku anglicky

Crude oil price prediction is a challenging task due to its complex nonlinear and chaotic behavior. There is a great need for oil price volatility measuring and modeling of oil price chaotic behavior. During the last couple of decades, both academiciansand practitioners have devoted proactive knowledge to address this issue. Combined predictors are one of the most promising forms in Machine learning (ML). It can be found in different styles in the literature such as Meta learning, Ensemble based prediction, Hybrid methods and more. The aim of this paper to conduct comprehensive comparisons among the combined prediction model in order to improve the performance.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

International Journal of Computer Information Systems and Industrial Management Applications

ISSN

2150-7988

e-ISSN

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Svazek periodika

7

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

53-65

Kód UT WoS článku

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EID výsledku v databázi Scopus

2-s2.0-84955438856