A Comparison of Artificial Neural Network and Time Series Models for Forecasting GDP in Palestine
American Journal of Theoretical and Applied Statistics
Volume 5, Issue 2, March 2016, Pages: 58-63
Received: Feb. 19, 2016; Accepted: Feb. 28, 2016; Published: Mar. 9, 2016
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Author
Samir K. Safi, Dept. of Economics and Statistics, Faculty of Commerce, the Islamic University of Gaza, Gaza, Palestine
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Abstract
Time series of quarterly observations on Gross Domestic Product (GDP) is collected and used in this study. Forecasting results of ANNs are compared with those of the Autoregressive Integrated Moving Average (ARIMA) and regression as benchmark methods. Using Root Mean Square Error (RMSE), the empirical results show that ANN performs better than the traditional methods in forecasting GDP.
Keywords
GDP, Artificial Neural Networks, Forecasting, ARIMA, Regression
To cite this article
Samir K. Safi, A Comparison of Artificial Neural Network and Time Series Models for Forecasting GDP in Palestine, American Journal of Theoretical and Applied Statistics. Vol. 5, No. 2, 2016, pp. 58-63. doi: 10.11648/j.ajtas.20160502.13
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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