Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (21): 139-144.DOI: 10.3778/j.issn.1002-8331.1908-0242

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Application of Mutual Information and Improved PCA Dimensionality Reduction Algorithm in Stock Price Forecasting

XIE Xinrui, LEI Xiuren, ZHAO Yan   

  1. 1.Department of Computational Mathematics, School of Mathematics, South China University of Technology, Guangzhou 510640, China
    2.Department of Probability Theory and Mathematical Statistics, School of Mathematics, South China University of Technology, Guangzhou 510640, China
  • Online:2020-11-01 Published:2020-11-03



  1. 1.华南理工大学 数学学院 信息与计算科学系,广州 510640
    2.华南理工大学 数学学院 统计与金融数学系,广州 510640


Considering the validity of a single feature on a tag and the information redundancy between multiple features, a method of mutual information combine with improving PCA for double dimensionality reduction are proposed. The mutual information is used to initially select a part of features from a large number of features, and some features that contribute less to the tag information are discarded. The principal component analysis method that uses the cumulative variance contribution rate and the multi-correlation coefficient to determine the number of principal elements is used for secondary dimensionality reduction. It not only ensures the information capacity of the principal component model, but also avoids the participation of excessive noise, thus ensuring the accuracy of the prediction process. The prediction of a single stock data through neural network shows the effectiveness of the dimensionality reduction algorithm proposed in this paper.

Key words: Mutual Information(MI), improved PCA, double dimensionality reduction, neural network prediction



关键词: 互信息, 改进PCA, 双重降维, 神经网络预测