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The Scientific World Journal
Volume 2014 (2014), Article ID 708918, 5 pages
Research Article

Application of Empirical Mode Decomposition with Local Linear Quantile Regression in Financial Time Series Forecasting

1School of Mathematical Sciences, Universiti Sains Malaysia, 11800 Minden, Penang, Malaysia
2Statistics Department, Sebha University, Sebha 00218, Libya

Received 15 January 2014; Revised 23 June 2014; Accepted 25 June 2014; Published 22 July 2014

Academic Editor: Mohamed Hanafi

Copyright © 2014 Abobaker M. Jaber 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.


This paper mainly forecasts the daily closing price of stock markets. We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ). We use the proposed technique, EMD-LLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared. The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.