An Effective Hybrid GA---PP Strategy for Artificial Neural Network Ensemble and Its Application Stock Market Forecasting

Authors:
Chunmei Wu;Jiansheng Wu
Affiliations:
Department of Mathematics and Computer, Liuzhou Teacher College Guangxi, Liuzhou, China;Department of Mathematics and Computer, Liuzhou Teacher College Guangxi, Liuzhou, China
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

Citing 7
Cited 0

Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
An investigation of neural networks for linear time-series forecasting

Computers and Operations Research
Neural Network Ensembles

IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates

Computers and Operations Research
A Projection Pursuit Algorithm for Exploratory Data Analysis

IEEE Transactions on Computers
Efficient prediction of exchange rates with low complexity artificial neural network models

Expert Systems with Applications: An International Journal
A novel nonlinear neural network ensemble model for financial time series forecasting

ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

The learning and generalizing ability of artificial neural network dependents on the particular training set. In this study, a novel hybrid GA---PP strategy for neural network ensemble model is proposed for stock market forecasting. First of all, we use the Projection Pursuit Technology based on Genetic Algorithms optimized to extract input factors, and then many individual neural networks are generated by Bagging techniques and different training way. Secondly, Projection Pursuit Technology based on Genetic Algorithm is used to select appropriate ensemble members. Finally, the logistic regress method is used for neural network ensemble. This method is established to forecast the Shanghai Stock Exchange index. The result shows that the ensemble network has reinforced the learning capacities and generalizing ability.