Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (8): 257-263.DOI: 10.3778/j.issn.1002-8331.2108-0433

• Engineering and Applications • Previous Articles     Next Articles

MTICA-AEO-SVR Model for Stock Price Forecasting

DENG Jiali, ZHAO Fengqun, WANG Xiaoxia   

  1. School of Sciences, Xi’an University of Technology, Xi’an 710054, China
  • Online:2022-04-15 Published:2022-04-15



  1. 西安理工大学 理学院,西安 710054

Abstract: In order to improve the stability and separation efficiency of traditional Fast ICA algorithm, a new nonlinear function based on Tukey M estimation is constructed in this paper, and then a MTICA algorithm is obtained. Furthermore, a novel MTICA-AEO-SVR model for stock price forecasting is established combining MTICA and SVR algorithms. Firstly, the original stock data is decomposed into independent components by MTICA algorithm for sorting and denoising, and then different SVR models are selected to predict the independent components and the stock price respectively. Artificial ecosystem optimization is introduced into the SVR algorithm to select parameters, as to improve the model prediction accuracy. The empirical results of the Shanghai B-share index show that MTICA-AEO-SVR model is more accurate and efficient than ICA-AEO-SVR model and ICA-SVR model in stock price prediction.

Key words: stock price prediction, independent component analysis(ICA), artificial ecosystem optimization(AEO), support vector regression(SVR)

摘要: 为了改善传统Fast ICA算法的稳定性和分离效率,基于Tukey M估计构造了一种新的非线性函数,提出了MTICA算法;并在此基础上结合SVR算法,建立了一种新的MTICA-AEO-SVR股票价格预测模型。用MTICA算法将原始股票数据分解为独立分量进行排序去噪,选择不同的SVR模型分别对各独立分量和股票价格进行预测。在SVR算法中引入了人工生态系统优化算法(AEO)选参,提高了模型的预测精度。通过对上证B股指数的实证分析,结果表明,MTICA-AEO-SVR模型比ICA-AEO-SVR模型和ICA-SVR模型更准确和高效。

关键词: 股票价格预测, 独立分量分析(ICA), 人工生态系统优化算法(AEO), 支持向量机回归(SVR)