A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates
Computers and Operations Research
Time-series forecasting using flexible neural tree model
Information Sciences: an International Journal
Adaptive smoothing neural networks in foreign exchange rate forecasting
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
Evolving flexible beta operator neural trees (FBONT) for time series forecasting
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Hi-index | 0.00 |
Forecasting exchange rates is an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. This paper proposes a Flexible Neural Tree (FNT) model for forecasting three major international currency exchange rates. Based on the pre-defined instruction sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using the Extended Compact Genetic Programming and the free parameters embedded in the neural tree are optimized by particle swarm optimization algorithm. Empirical results indicate that the proposed method is better than the conventional neural network forecasting models.