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A Comparative Analysis of Artificial Neural Network and Autoregressive Integrated Moving Average Model on Modeling and Forecasting Exchange Rate

Authors: Mogari I. Rapoo, Diteboho Xaba

Abstract:

This paper examines the forecasting performance of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) models with the published exchange rate obtained from South African Reserve Bank (SARB). ARIMA is one of the popular linear models in time series forecasting for the past decades. ARIMA and ANN models are often compared and literature revealed mixed results in terms of forecasting performance. The study used the MSE and MAE to measure the forecasting performance of the models. The empirical results obtained reveal the superiority of ARIMA model over ANN model. The findings further resolve and clarify the contradiction reported in literature over the superiority of ARIMA and ANN models.

Keywords: ARIMA, artificial neural networks models, error metrics, exchange rates.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1132673

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References:


[1] Adhikari, R. and Agrawal, R. K., 2013. An introductory study on time series modeling and forecasting. arXiv preprint arXiv:1302.6613.
[2] Binner*, J. M., Bissoondeeal, R. K., Elger, T., Gazely, A. M. and Mullineux, A. W. 2005. A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia. Applied economics, 37(6), pp.665-680.
[3] Bissoondeeal, R. K., Binner, J. M., Bhuruth, M., Gazely, A. and Mootanah, V. P. 2008. Forecasting exchange rates with linear and nonlinear models. Global Business and Economics Review, 10(4), pp.414-429.
[4] Brown, R. L., Durbin, J. and Evans, J. M. 1975. Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society. Series B (Methodological):149-192.
[5] Clements, M. P., Franses, P. H. and Swanson, N. R. 2004. Forecasting economic and financial time-series with non-linear models. International Journal of Forecasting, 20(2), pp.169-183.
[6] Jha, G. K. and Sinha, K. 2013. Agricultural price forecasting using neural network model: An innovative information delivery system. Agricultural Economics Research Review, 26(2), pp.229-239.
[7] Kaastra, I. and Boyd, M. 1996. Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10(3), pp.215-236.
[8] Kihoro, J. M., Otieno, R. O. and Wafula, C. 2004. Seasonal time series forecasting: A comparative study of ARIMA and ANN models. AJST, 5(2).
[9] Liu, H., Tian, H. Q. and Li, Y. F. 2012. Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction. Applied Energy, 98, pp.415-424.
[10] Nourbakhsh, F., Rostami, A., Arshadi, A., Sarlak, A. and Almasi, M. 2013. Comparative Study of Artificial Neural Networks ANN and ARIMA Method, in Predicting the Overall Index of Tehran Stock Exchange. Journal of Basic and Applied Scientific Research, 3(3), pp.319-324
[11] Oyewale, A. M. 2013. Evaluation of artificial neural networks in foreign exchange forecasting. American Journal of Theoretical and Applied Statistics, 2(4), pp.94-101.
[12] Pedram, M. and Ebrahimi, M. 2014. Exchange Rate Model Approximation, Forecast and Sensitivity Analysis by Neural Networks, Case of Iran. Business and Economic Research, 4(2), p.49.
[13] Singh, A. and Mishra, G.C. 2015. Application of Box-Jenkins method and Artificial Neural Network procedure for time series forecasting of prices. Statistics in Transition new series, 1(16), pp.83-96.
[14] Stokes, A. 2011. Forecasting exchange rates using neural networks: A trader's approach.
[15] Zhang, G. P. 2003. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, pp.159-175.