TY  - JOUR
T1  - Exchange Rate Prediction Using Neural-Genetic Model
AU - Mechgoug, R. AU - Titaouine, A. 
JO  - International Journal of Soft Computing
VL  - 7
IS  - 3
SP  - 120
EP  - 125
PY  - 2012
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2012.120.125
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2012.120.125
KW  - Prediction
KW  -time series
KW  -foreign exchange
KW  -neural network
KW  -genetic algorithm
AB  - 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.
ER  -