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

Samir K. Safi

doi:doi:10.11648/j.ajtas.20160502.13

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

Samir K. Safi

Published in American Journal of Theoretical and Applied Statistics (Volume 5, Issue 2)

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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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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TY  - JOUR
T1  - A Comparison of Artificial Neural Network and Time Series Models for Forecasting GDP in Palestine
AU  - Samir K. Safi
Y1  - 2016/03/09
PY  - 2016
N1  - https://doi.org/10.11648/j.ajtas.20160502.13
DO  - 10.11648/j.ajtas.20160502.13
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 58
EP  - 63
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20160502.13
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.
VL  - 5
IS  - 2
ER  -

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Author Information

Samir K. Safi

Dept. of Economics and Statistics, Faculty of Commerce, the Islamic University of Gaza, Gaza, Palestine

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Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

TY  - JOUR
T1  - A Comparison of Artificial Neural Network and Time Series Models for Forecasting GDP in Palestine
AU  - Samir K. Safi
Y1  - 2016/03/09
PY  - 2016
N1  - https://doi.org/10.11648/j.ajtas.20160502.13
DO  - 10.11648/j.ajtas.20160502.13
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 58
EP  - 63
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20160502.13
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.
VL  - 5
IS  - 2
ER  -

Copy | Download