FORECASTING THE CRUDE OIL SPOT PRICE BY WAVELET NEURAL NETWORKS USING OECD PETROLEUM INVENTORY LEVELS

Ye Pang (), Wei Xu (), Lean Yu (), Jian Ma (), Kin Keung Lai (), Shouyang Wang () and Shanying Xu ()
Additional contact information
Ye Pang: Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
Wei Xu: School of Management, Graduate University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, 100190, China
Jian Ma: Department of Information Systems, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
Kin Keung Lai: Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
Shouyang Wang: Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
Shanying Xu: Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

New Mathematics and Natural Computation (NMNC), 2011, vol. 07, issue 02, 281-297

Abstract: In this study, a novel forecasting model based on the Wavelet Neural Network (WNN) is proposed to predict the monthly crude oil spot price. In the proposed model, the OECD industrial petroleum inventory level is used as an independent variable, and the Wavelet Neural Network (WNN) is used to explore the nonlinear relationship between inventories and the price. For verification purposes, the West Texas Intermediate (WTI) crude oil spot price is used for the tested target. Experimental results reveal that the WNN can model the nonlinear relationship between inventories and the price very well. Furthermore, the in-sample and out-of-sample prediction performance also demonstrates that the WNN-based forecasting model can produce more accurate prediction results than other nonlinear and linear models, even when the lengths of the forecast horizon are relatively short or long.

Keywords: Crude oil price forecasting; inventory level; wavelet neural network (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (3)

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DOI: 10.1142/S1793005711001937

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