The nature of statistical learning theory
The nature of statistical learning theory
Feature extraction through LOCOCODE
Neural Computation
An investigation of neural networks for linear time-series forecasting
Computers and Operations Research
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
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
Financial time series forecasting using independent component analysis and support vector regression
Decision Support Systems
Integrating Nonlinear Independent Component Analysis and Neural Network in Stock Price Prediction
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Electric load forecasting based on locally weighted support vector regression
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Predicting stock index using an integrated model of NLICA, SVR and PSO
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
| Hi-index | 0.01 |
Forecasting stock prices is a major activity of financial firms and private investors. In developing a stock price forecasting model, the first step is usually feature extraction. Nonlinear independent component analysis (NLICA), a novel feature extraction technique that assumes the observed mixtures are non-linear combinations of latent source signals, is used to find independent sources when observed data are mixtures of unknown sources, and prior knowledge of the mixing mechanisms is not available. In this paper, a stock price forecasting model which first uses NLICA as preprocessing to extract features from forecasting variables is developed. Then the features, called independent components (ICs), serve as the inputs of support vector regression (SVR) to build the forecasting model. The advantage of the proposed methodology is that the information hidden in the original data can be discovered by feature extraction. Therefore, NLICA can provide more valuable information for financial forecasting. Two datasets of major Asian stock markets-China and Japan, Shanghai Stock Exchange Composite (SSEC) and Nikkei 225 stock indexes, are used as illustrative examples. For comparison, the integration of traditional principal component analysis (PCA) with SVR (called PCA-SVR), linear ICA with SVR (called LICA-SVR) and single SVR approaches were applied to evaluate the prediction accuracy of the proposed approach. Empirical results show that the proposed method (NLICA-SVR) not only improves the prediction accuracy of the SVR approach but also outperforms the PCA-SVR, LICA-SVR and single SVR methods.