A reliability-based RBF network ensemble model for foreign exchange rates predication

Authors:
Lean Yu;Wei Huang;Kin Keung Lai;Shouyang Wang
Affiliations:
Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China and Department of Management Sciences, City University of Hong Kong, Kowloon, ...;School of Management, Huazhong University of Science and Technology, Wuhan, China;Department of Management Sciences, City University of Hong Kong, Kowloon, Hong Kong and College of Business Administration, Hunan University, Changsha, China;Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China and College of Business Administration, Hunan University, Changsha, China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

Citing 8
Cited 3

Unstable weights in the combination of forecasts

Management Science
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
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
Improving the generalization properties of radial basis function neural networks

Neural Computation
Handwritten digit recognition using k nearest-neighbor, radial-basis function, and backpropagation neural networks

Neural Computation
Parallel consensual neural networks

IEEE Transactions on Neural Networks

Multistage RBF neural network ensemble learning for exchange rates forecasting

Neurocomputing
Orthogonal support vector machine for credit scoring

Engineering Applications of Artificial Intelligence
Short-term wind speed forecasting based on a hybrid model

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this study, a reliability-based RBF neural network ensemble forecasting model is proposed to overcome the shortcomings of the existing neural ensemble methods and ameliorate forecasting performance. In this model, the ensemble weights are determined by the reliability measure of RBF network output. For testing purposes, we compare the new ensemble model's performance with some existing network ensemble approaches in terms of three exchange rates series. Experimental results reveal that the prediction using the proposed approach is consistently better than those obtained using the other methods presented in this study in terms of the same measurements.