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Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this m...

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Main Authors: Shabri, Ani, Samsudin, Ruhaidah
Format: Article
Language:English
Published: The Scientific World Journal 2014
Subjects:
Q Science
Online Access:http://eprints.utm.my/id/eprint/52267/1/AniShabri2014_CrudeOilPriceForecastingBasedOnHbridizing.pdf
http://eprints.utm.my/id/eprint/52267/
http://dx.doi.org/10.1155/2014/854520
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spelling my.utm.522672018-09-17T04:08:01Z http://eprints.utm.my/id/eprint/52267/ Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis Shabri, Ani Samsudin, Ruhaidah Q Science Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series The Scientific World Journal 2014 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/52267/1/AniShabri2014_CrudeOilPriceForecastingBasedOnHbridizing.pdf Shabri, Ani and Samsudin, Ruhaidah (2014) Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis. Scientific World Journal . ISSN 1537-744X http://dx.doi.org/10.1155/2014/854520 DOI: 10.1155/2014/854520
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic Q Science
spellingShingle Q Science
Shabri, Ani
Samsudin, Ruhaidah
Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis
description Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series
format Article
author Shabri, Ani
Samsudin, Ruhaidah
author_facet Shabri, Ani
Samsudin, Ruhaidah
author_sort Shabri, Ani
title Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis
title_short Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis
title_full Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis
title_fullStr Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis
title_full_unstemmed Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis
title_sort crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis
publisher The Scientific World Journal
publishDate 2014
url http://eprints.utm.my/id/eprint/52267/1/AniShabri2014_CrudeOilPriceForecastingBasedOnHbridizing.pdf
http://eprints.utm.my/id/eprint/52267/
http://dx.doi.org/10.1155/2014/854520
_version_ 1643653176613666816
score 13.214268

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