a hybrid least square support vector machine model with parameters optimization for stock forecasting

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ID: 209181
2015
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Abstract
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.
Reference Key
chai2015mathematicala Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Jian Chai;Jiangze Du;Kin Keung Lai;Yan Pui Lee
Journal journal of power sources
Year 2015
DOI
10.1155/2015/231394
URL
http://dx.doi.org/10.1155/2015/231394
Keywords
civil engineering (general)mathematics engineering (general)

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