Factor analysis and principal components
Journal of Multivariate Analysis
Independent component analysis: algorithms and applications
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Image denoising using self-organizing map-based nonlinear independent component analysis
Neural Networks - New developments in self-organizing maps
MISEP - Linear and nonlinear ICA based on mutual information
The Journal of Machine Learning Research
Computers and Operations Research
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Nonlinear independent component analysis with minimal nonlinear distortion
Proceedings of the 24th international conference on Machine learning
Surveying stock market forecasting techniques - Part II: Soft computing methods
Expert Systems with Applications: An International Journal
A neural network with a case based dynamic window for stock trading prediction
Expert Systems with Applications: An International Journal
Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network
Expert Systems with Applications: An International Journal
Financial time series forecasting using independent component analysis and support vector regression
Decision Support Systems
Expert Systems with Applications: An International Journal
Evaluation approach to stock trading system using evolutionary computation
Expert Systems with Applications: An International Journal
Stock indices prediction using radial basis function neural network
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Hi-index | 12.05 |
With the economic successes of several Asian economies and their increasingly important roles in the global financial market, the prediction of Asian stock markets has becoming a hot research area. As Asian stock markets are highly dynamic and exhibit wide variation, it may more realistic and practical that assumed the stock indexes of Asian stock markets are nonlinear mixture data. In this research, a time series prediction model by combining nonlinear independent component analysis (NLICA) and neural network is proposed to forecast Asian stock markets. NLICA is a novel feature extraction technique to find independent sources from observed nonlinear mixture data where no relevant data mixing mechanisms are available. In the proposed method, we first use NLICA to transform the input space composed of original time series data into the feature space consisting of independent components representing underlying information of the original data. Then, the ICs are served as the input variables of the neural network to build prediction model. Among the Asian stock markets, Japanese and China's stock markets are the biggest two in Asia and they respectively represent the two types of stock markets. Therefore, in order to evaluate the performance of the proposed approach, the Nikkei 225 closing index and Shanghai B-share closing index are used as illustrative examples. Experimental results show that the proposed forecasting model not only improves the prediction accuracy of the neural network approach but also outperforms the three comparison methods. The proposed stock index prediction model can be therefore a good alternative for Asian stock market indexes.