Unstable weights in the combination of forecasts
Management Science
The Strength of Weak Learnability
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
Machine Learning
Optimal Linear Combination of Neural Networks for Improving Classification Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
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 reliability-based RBF network ensemble model for foreign exchange rates predication
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
A bias-variance-complexity trade-off framework for complex system modeling
ICCSA'06 Proceedings of the 6th international conference on Computational Science and Its Applications - Volume Part I
Parallel consensual neural networks
IEEE Transactions on Neural Networks
Applying text and data mining techniques to forecasting the trend of petitions filed to e-People
Expert Systems with Applications: An International Journal
A semiparametric regression ensemble model for rainfall forecasting based on RBF neural network
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
A forecasting system for car fuel consumption using a radial basis function neural network
Expert Systems with Applications: An International Journal
Short-term wind speed forecasting based on a hybrid model
Applied Soft Computing
PREDICTION & WARNING: a method to improve student's performance
ACM SIGSOFT Software Engineering Notes
Hi-index | 0.01 |
In this study, a multistage nonlinear radial basis function (RBF) neural network ensemble forecasting model is proposed for foreign exchanger rates prediction. In the process of ensemble modeling, the first stage produces a great number of single RBF neural network models. In the second stage, a conditional generalized variance (CGV) minimization method is used to choose the appropriate ensemble members. In the final stage, another RBF network is used for neural network ensemble for prediction purpose. For testing purposes, we compare the new ensemble model's performance with some existing neural network ensemble approaches in terms of four exchange rates series. Experimental results reveal that the predictions using the proposed approach are consistently better than those obtained using the other methods presented in this study in terms of the same measurements.