Research on Predicted Model of Least Squares Support Vector Machine Based on Genetic Algorithm

Abstract:

There is huge amount of data with complex uncertainty in the stock market. Meanwhile, efficient stock prediction is important in financial investment. This paper puts forward a classified and predicted model based on least squares support vector machine (LS-SVM) in the background of stock investment. This model preprocesses the input vector of stock indexes using the method of Wilcoxon symbols test and factor analysis, and determines the parameter of LS-SVM based on the genetic algorithm, after that classifies the stocks based on growth rate, then is trained using the stock sample. At last this paper verifies the model with the samples. It also presents a demo to predict the increasing trend of the stock. The result shows that this model owns favorable predicted ability with high correct classification rate.

• Periodical: Advanced Materials Research (Volumes 753-755)
• Pages: 2875-2881
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.753-755.2875
• Online since: August 2013
• Authors: Huai Lin Dong, Juan Juan Huang, Zhu Hua Cai, Qing Feng Wu*
• Keywords: Classified and Predicted Model, Genetic Algorithm (GA), Least Squares Support Vector Machines (Ls-SVM)

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