title: Application of empirical mode decomposition with local linear quantile regression in financial time series forecasting.
abstract: 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.
authors.affiliation: School of Mathematical Sciences, Universiti Sains Malaysia, 11800 Minden, Penang, Malaysia.
authors.forename: Abobaker M
authors.initials: AM
authors.lastname: Jaber
authors.affiliation: School of Mathematical Sciences, Universiti Sains Malaysia, 11800 Minden, Penang, Malaysia.
authors.forename: Mohd Tahir
authors.initials: MT
authors.lastname: Ismail
authors.affiliation: Statistics Department, Sebha University, Sebha 00218, Libya.
authors.forename: Alsaidi M
authors.initials: AM
authors.lastname: Altaher
mesh_terms: D000465:Algorithms; D003132:Commerce / Q000639:trends*; D003198:Computer Simulation; D005544:Forecasting; D012044:Regression Analysis