Research Article Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model Ani Shabri 1 and Ruhaidah Samsudin 2 1 Department of Science Mathematic, Faculty of Science, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia 2 Department of Sofware Engineering, Faculty of Computing, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, Malaysia Correspondence should be addressed to Ani Shabri; ani sabri@hotmail.com Received 24 January 2014; Revised 2 July 2014; Accepted 2 July 2014; Published 16 July 2014 Academic Editor: Marek Lefk Copyright © 2014 A. Shabri and R. Samsudin. Tis 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 artifcial neural networks (WANN) model for daily crude oil price forecasting is proposed. Te discrete Mallat wavelet transform is used to decompose the crude price series into one approximation series and some details series (DS). Te new series obtained by adding the efective one approximation series and DS component is then used as input into the ANN model to forecast crude oil price. Te 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. 1. Introduction Crude oil prices fuctuations are of signifcant interest to both fnancial practitioners and market participants. However, crude oil price is one of the most complex and difcult to model because fuctuation of the crude oil price is rather irregular, nonlinear, nonstationary, and with high volatility. Tus, accurate forecasting of the crude oil price time series is one of the greatest challenges and among the most important issues facing energy economists towards better decisions in several managerial levels. For this reason, many researchers have devoted considerable efort to the development of diferent types of models for crude oil price forecasting. Te application of the classical time series models such as autoregressive moving average (ARMA) model (Moham- madi and Su [1], Ahmad [2], Wang et al. [3], and Xie et al. [4]) and generalized autoregressive conditional heteroscedastic (GARCH) type models (Morana [5], Sadorsky [6], and Agnolucci [7]) for crude oil price forecasting has received much attention in the last decade. However, the above models can provide good prediction results, when the price series under study is basically linear or near linear, and have a limited ability to capture nonlinearity and nonstationary in crude oil prices data. Artifcial neural network (ANN) techniques have shown great ability in modeling and forecasting nonlinear and complex time series. ANN ofers an efective approach for handling large amounts of dynamic, nonlinear, and noise data. Numerous papers have already presented successful application of ANN for modeling and forecasting the crude oil price series (Lackes et al. [8], Khazem and Mazouz [9], Mirmirani and Li [10], Kulkarni and Haidar [11], Yu et al. [12], and Hu et al. [13]) as well as for forecasting real world time series. Teir experimental results show that the performance of ANN is superior to various traditional statistical models. ANN has an ability to learn complex and nonlinear time series that is difcult to model with conventional models. However, there are some disadvantages of ANN. Although ANN has advantages of accurate forecasting, their perfor- mance in some specifc situation is inconsistent (Khashei and Bijari [14]). ANN also ofen sufers from local minima and overftting, and the network structure of this model is difcult to determine and it is usually determined by using a trial- and-error approach (Kis ¸i [15]). In addition, ANN model has Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2014, Article ID 201402, 10 pages http://dx.doi.org/10.1155/2014/201402