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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 201402, 10 pages
Research Article

Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model

1Department of Science Mathematic, Faculty of Science, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia
2Department of Software Engineering, Faculty of Computing, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, Malaysia

Received 24 January 2014; Revised 2 July 2014; Accepted 2 July 2014; Published 16 July 2014

Academic Editor: Marek Lefik

Copyright © 2014 Ani Shabri and Ruhaidah Samsudin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A new method based on integrating discrete wavelet transform and artificial neural networks (WANN) model for daily crude oil price forecasting is proposed. The discrete Mallat wavelet transform is used to decompose the crude price series into one approximation series and some details series (DS). The new series obtained by adding the effective one approximation series and DS component is then used as input into the ANN model to forecast crude oil price. The relative performance of WANN model was compared to regular ANN model for crude oil forecasting at lead times of 1 day for two main crude oil price series, West Texas Intermediate (WTI) and Brent crude oil spot prices. In both cases, WANN model was found to provide more accurate crude oil prices forecasts than individual ANN model.