Exchange Rate Prediction Using Neural-Genetic Model

摘要

Neural network have successfully used for exchange rate forecasting. However, due to a large number of parameters to be estimated empirically, it is not a simple task to select the appropriate neural network architecture for exchange rate forecasting problem. Researchers often overlook the effect of neural network parameters on the performance of neural network forecasting. The performance of neural network is critically dependant on the learning algorithms, the network architecture and the choice of the control parameters. Even when a suitable setting of parameters (weight) can be found, the ability of the resulting network to generalize the data not seen during learning may be far from optimal. For these reasons, it seems logical and attractive to apply genetic algorithms. Genetic algorithms may provide a useful tool for automating the design of neural network. The empirical results on foreign exchange rate prediction indicate that the proposed Hybrid Model exhibits effectively improved accuracy when is compared with some other time series forecasting models.