Exchange Rate Prediction Using an Evolutionary Connectionist Model

Artificial neural networks (ANNs) have been applied to time series forecasting. Genetic algorithm (GA) can be used as an optimization search scheme to determine the near optimal architecture and parameters of a neural network, as well. In this study a rich evolutionary connectionist model is proposed, in which GA is used to determine the optimum number of input and hidden nodes of a feedforward neural network, the optimum slope of nodes’ activation function and the optimum values of learning rates and momentum coefficients. 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.