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Mathematical Problems in Engineering
Volume 2014, Article ID 513201, 9 pages
Research Article

Multiscale Combined Model Based on Run-Length-Judgment Method and Its Application in Oil Price Forecasting

School of Economics and Management, North China University of Technology, Beijing 100144, China

Received 30 December 2013; Revised 15 April 2014; Accepted 14 May 2014; Published 12 June 2014

Academic Editor: Jianping Li

Copyright © 2014 Wang Shu-ping 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.


Forecasting of oil price is an important area of energy market research. Based on the idea of decomposition-reconstruction-integration, this paper built a new multiscale combined forecasting model with the methods of empirical mode decomposition (EMD), artificial neural network (ANN), support vector machine (SVM), and time series methods. While building the model, we proposed a new idea to use run length judgment method to reconstruct the component sequences. Then this model was applied to analyze the fluctuation and trend of international oil price. Oil price series was decomposed and reconstructed into high frequency, medium frequency, low frequency, and trend sequences. Different features of fluctuation can be explained by irregular factors, season factors, major events, and long-term trend. Empirical analysis showed that the multiscale combined model obtained the best forecasting result compared with single models including ARIMA, Elman, SVM, and GARCH and combined models including ARIMA-SVM model and EMD-SVM-SVM method.