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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

11 Scopus Citations
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Author(s)

  • Jian Chai
  • Jiangze Du
  • Kin Keung Lai
  • Yan Pui Lee

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Detail(s)

Original languageEnglish
Article number231394
Journal / PublicationMathematical Problems in Engineering
Volume2015
Publication statusPublished - 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.

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