Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 257947, 11 pages
Research Article

Hybrid Soft Computing Schemes for the Prediction of Import Demand of Crude Oil in Taiwan

1Department of Statistics and Information Science, Fu Jen Catholic University, Xinzhuang, New Taipei 24205, Taiwan
2Department of Industrial Management, Chien Hsin University of Science and Technology, Zhongli, Taoyuan 32097, Taiwan

Received 18 February 2014; Accepted 7 April 2014; Published 28 April 2014

Academic Editor: Ker-Wei Yu

Copyright © 2014 Yuehjen E. Shao et al. 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.


Crude oil is the most important nonrenewable energy resource and the most key element for the world. In contrast to typical forecasts of oil price, this study aims at forecasting the demand of imported crude oil (ICO). This study proposes different single stage and two-stage hybrid stages of forecasting models for prediction of ICO in Taiwan. The single stage forecasting modeling includes multiple linear regression (MLR), support vector regression (SVR), artificial neural networks (ANN), and extreme learning machine (ELM) approaches. While the first step of the two-stage modeling is to select the fewer but more significant explanatory variables, the second step is to generate predictions by using these significant explanatory variables. The proposed two-stage hybrid models consist of integration of different modeling components. Mean absolute percentage error, root mean square error, and mean absolute difference are utilized as the performance measures. Real data set of crude oil in Taiwan for the period of 1993–2010 and twenty-three associated explanatory variables are sampled and investigated. The forecasting results reveal that the proposed two-stage hybrid modeling is able to accurately predict the demand of crude oil in Taiwan.