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A Comparison of Artificial Neural Network and Time Series Models for Forecasting GDP in Palestine

Received: 19 February 2016     Accepted: 28 February 2016     Published: 9 March 2016
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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.

Published in American Journal of Theoretical and Applied Statistics (Volume 5, Issue 2)
DOI 10.11648/j.ajtas.20160502.13
Page(s) 58-63
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

GDP, Artificial Neural Networks, Forecasting, ARIMA, Regression

References
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[3] Box, G. E. P. & Jenkins, G. M. Time series analysis, forecasting and control. San Francisco: Holden-Day, 1976.
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[5] Cadenas E. & Rivera W. Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks. Renew Energy, 2009, 34(1): 274-8.
[6] Cryer, J. D. & Chan, K. S. Time series analysis with applications in R, Second Edition. New York: Springer, 2008.
[7] Hamid, S. A., & Habib, A. Financial Forecasting with Neural Networks. Academy of Accounting and Financial Studies Journal. 2014, 8(4).‏
[8] KÖLMEK, M. A., & Navruz, I. Forecasting the day-ahead price in electricity balancing and settlement market of Turkey by using artificial neural networks. Turkish Journal of Electrical Engineering & Computer Sciences, 2015, 23(3).‏
[9] Lee, T. S. & Chen, I. F. A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Expert Systems with Applications, 2005, 28(4): 743–752.
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  • APA Style

    Samir K. Safi. (2016). A Comparison of Artificial Neural Network and Time Series Models for Forecasting GDP in Palestine. American Journal of Theoretical and Applied Statistics, 5(2), 58-63. https://doi.org/10.11648/j.ajtas.20160502.13

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    ACS Style

    Samir K. Safi. A Comparison of Artificial Neural Network and Time Series Models for Forecasting GDP in Palestine. Am. J. Theor. Appl. Stat. 2016, 5(2), 58-63. doi: 10.11648/j.ajtas.20160502.13

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    AMA Style

    Samir K. Safi. A Comparison of Artificial Neural Network and Time Series Models for Forecasting GDP in Palestine. Am J Theor Appl Stat. 2016;5(2):58-63. doi: 10.11648/j.ajtas.20160502.13

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  • @article{10.11648/j.ajtas.20160502.13,
      author = {Samir K. Safi},
      title = {A Comparison of Artificial Neural Network and Time Series Models for Forecasting GDP in Palestine},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {5},
      number = {2},
      pages = {58-63},
      doi = {10.11648/j.ajtas.20160502.13},
      url = {https://doi.org/10.11648/j.ajtas.20160502.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20160502.13},
      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.},
     year = {2016}
    }
    

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    AB  - 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.
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Author Information
  • Dept. of Economics and Statistics, Faculty of Commerce, the Islamic University of Gaza, Gaza, Palestine

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