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

This paper mainly forecasts the daily closing price of stockmarkets.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, EMDLLQ, to forecast two stock index time series. Detail...

Full description

Bibliographic Details
Main Authors: M. Jaber, Abobaker, Ismail, Mohd Tahir, M. Altaher, Alsaidi
Format: Article
Language:English
Published: Hindawi Publishing Corporation
Subjects:
Online Access:http://eprints.usm.my/38348/1/Application_of_Empirical_Mode_Decomposition_with_Local_Linear_Quantile_Regression_in.pdf
Description
Summary:This paper mainly forecasts the daily closing price of stockmarkets.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, EMDLLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposedmethod, in which EMD-LPQ, EMD, andHolt-Winter methods are compared.The proposed EMD-LPQ model is determined to be superior to the EMDandHolt- Winter methods in predicting the stock closing prices.