Using MLP and RBF Neural Networks to Improve the Prediction of Exchange Rate Time Series with ARIMA
Keywords:
ARIMA; multilayer perceptrons; radial basis functions; time series forecastingAbstract
In this paper , a new hybrid model for predicting
the exchange rate time series is introduced, using the multilayer perceptron (MLP) and radial basis function (RBF) neural networks to reduce the error of autoregressive integrated moving average (ARIMA) method. The hybrid model tries to detect the error of the linear statistical method and then model this error with MLP neural network. Again the remainder error is modeled with RBF neural network to reduce the final
error of the hybrid model. In this two level process of error modeling, it will be proved that the final result of prediction and modeling is better than the results that could be achieved by a single ARIMA method or a single MLP or RBF neural network.
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