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A Hybrid Least Square Support Vector Machine Model with Parameters Optimization for Stock Forecasting
Joint Authors
Lai, Kin Keung
Chai, Jian
Du, Jiangze
Lee, Yan Pui
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-01-19
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
This paper proposes an EMD-LSSVM (empirical mode decomposition least squares support vector machine) model to analyze the CSI 300 index.
A WD-LSSVM (wavelet denoising least squares support machine) is also proposed as a benchmark to compare with the performance of EMD-LSSVM.
Since parameters selection is vital to the performance of the model, different optimization methods are used, including simplex, GS (grid search), PSO (particle swarm optimization), and GA (genetic algorithm).
Experimental results show that the EMD-LSSVM model with GS algorithm outperforms other methods in predicting stock market movement direction.
American Psychological Association (APA)
Chai, Jian& Du, Jiangze& Lai, Kin Keung& Lee, Yan Pui. 2015. A Hybrid Least Square Support Vector Machine Model with Parameters Optimization for Stock Forecasting. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1073269
Modern Language Association (MLA)
Chai, Jian…[et al.]. A Hybrid Least Square Support Vector Machine Model with Parameters Optimization for Stock Forecasting. Mathematical Problems in Engineering No. 2015 (2015), pp.1-7.
https://search.emarefa.net/detail/BIM-1073269
American Medical Association (AMA)
Chai, Jian& Du, Jiangze& Lai, Kin Keung& Lee, Yan Pui. A Hybrid Least Square Support Vector Machine Model with Parameters Optimization for Stock Forecasting. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1073269
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1073269