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

Author

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  • Catalina Lucia COCIANU
  • Mihai-Serban AVRAMESCU

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.

Suggested Citation

  • Catalina Lucia COCIANU & Mihai-Serban AVRAMESCU, 2018. "New Approaches of NARX-Based Forecasting Model. A Case Study on CHF-RON Exchange Rate," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 22(2), pages 5-13.
    • Handle: RePEc:aes:infoec:v:22:y:2018:i:2:p:5-13
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    References listed on IDEAS

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    1. Adam Stokes & Ahmed S. Abou-Zaid, 2012. "Forecasting foreign exchange rates using artificial neural networks: a trader's approach," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 5(4), pages 370-394.
    2. Tamal Datta Chaudhuri & Indranil Ghosh, 2016. "Artificial Neural Network and Time Series Modeling Based Approach to Forecasting the Exchange Rate in a Multivariate Framework," Papers 1607.02093, arXiv.org.
    3. Cătălina-Lucia COCIANU & Hakob GRIGORYAN, 2016. "Machine Learning Techniques For Stock Market Prediction.Acase Study Of Omv Petrom," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(3), pages 63-82.
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