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

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

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Official URL: http://dx.doi.org/10.1155/2014/854520

Abstract

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

Item Type:Article
Uncontrolled Keywords:forecasting, linear models, models, theoretical, petroleum, principal component analysis
Subjects:Q Science
Divisions:Science
ID Code:52267
Deposited By: Siti Nor Hashidah Zakaria
Deposited On:01 Feb 2016 11:52
Last Modified:17 Sep 2018 12:08

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