New Approaches of NARX-Based Forecasting Model. A Case Study on CHF-RON Exchange Rate

Abstract

The work reported in the paper focuses on the prediction of the exchange rate of the Swiss Franc-Romanian Leu against the US Dollar-Romanian Leu using the NARX model. We propose two new forecasting methods based on NARX model by considering both additional testing and network retraining in order to improve the generalization capacities of the trained neural network. The forecasting accuracy of the two methods is evaluated in terms of one of the most popular quality measure, namely weighted RMSE error. The comparative analysis together with experimental results and conclusive remarks are reported in the final part of the paper. The performances of the proposed methodologies are evaluated by a long series of tests, the results being very encouraging as compared to similar developments. Based on the conducted experiments, we conclude that both resulted algorithms perform better than the classical one. Moreover, the retraining method in which the network is conserved over time outperforms the one in which only additional testing is used.