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

被引：4

作者：

Jaber, Abobaker M. ^{[1
]}

Ismail, Mohd Tahir ^{[1
]}

Altaher, Alsaidi M. ^{[2
]}

机构：

[1] Univ Sains Malaysia, Sch Math Sci, Minden 11800, Penang, Malaysia

[2] Sebha Univ, Dept Stat, Sebha 00218, Libya

来源：

SCIENTIFIC WORLD JOURNAL | 2014年

关键词：

D O I：

10.1155/2014/708918

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：5

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