Ten lectures on wavelets
Performance of neural networks in managerial forecasting
International Journal of Intelligent Systems in Accounting and Finance Management - Special issue on neural networks
An introduction to wavelets
The nature of statistical learning theory
The nature of statistical learning theory
Neural network models for time series forecasts
Management Science
Applied Wavelet Analysis with S-Plus
Applied Wavelet Analysis with S-Plus
Walvelet Analysis of Commodity Price Behavior
Computational Economics
A Stochastic Nonlinear Regression Estimator Using Wavelets
Computational Economics
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Multi-agent modeling of multiple FX-markets by neural networks
IEEE Transactions on Neural Networks
Chaos-based support vector regressions for exchange rate forecasting
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Using Gaussian process based kernel classifiers for credit rating forecasting
Expert Systems with Applications: An International Journal
Neural network ensemble model using PPR and LS-SVR for stock market forecasting
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Save the best for last? The treatment of dominant predictors in financial forecasting
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
By integrating genetic algorithm (GA)-based optimal time-scale feature extractions with support vector machines (SVM), this study develops a novel hybrid prediction model that operates for multiple time-scale resolutions and utilizes a flexible nonparametric regressor to predict future evolutions of various stock indices. The time series of explanatory variables are decomposed using wavelet bases, and a GA is employed to extract optimal time-scale feature subsets from decomposed features. These extracted time-scale feature subsets then serve as an input for an SVM model that performs final forecasting. Compared with neural networks, pure SVMs or traditional GARCH models, the proposed model performs best. The root-mean-squared forecasting errors are significantly reduced.