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DOI: 10.14569/IJACSA.2014.050429
PDF

Modeling and Forecasting the Number of Pilgrims Coming from Outside the Kingdom of Saudi Arabia Using Bayesian and Box-Jenkins Approaches

Author 1: SAMEER M. SHAARAWY
Author 2: ESAM A. KHAN
Author 3: MAHMOUD A. ELGAMAL

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 5 Issue 4, 2014.

  • Abstract and Keywords
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Abstract: Pilgrimage has received a great attention by the government of Saudi Arabia. Of special interest is the yearly series of the Number of Pilgrims coming from Outside the kingdom (NPO) since it is one of the most important indicators in determining the planning mechanism for future hajj seasons. This study approaches the problems of identification, estimation, diagnostic checking and forecasting of the NPO series using Bayesian and Box - Jenkins approaches. The accuracy of Bayesian and Box- Jenkins techniques have been checked for forecasting the future observations and the results were very satisfactory. Moreover, it has been shown that Bayesian technique gives more accurate results than Box-Jenkins technique.

Keywords: autoregressive processes; identification; estimation; diagnostic checking; forecasting; Jeffreys' prior; and posterior probability mass function

SAMEER M. SHAARAWY, ESAM A. KHAN and MAHMOUD A. ELGAMAL, “Modeling and Forecasting the Number of Pilgrims Coming from Outside the Kingdom of Saudi Arabia Using Bayesian and Box-Jenkins Approaches” International Journal of Advanced Computer Science and Applications(IJACSA), 5(4), 2014. http://dx.doi.org/10.14569/IJACSA.2014.050429

@article{SHAARAWY2014,
title = {Modeling and Forecasting the Number of Pilgrims Coming from Outside the Kingdom of Saudi Arabia Using Bayesian and Box-Jenkins Approaches},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2014.050429},
url = {http://dx.doi.org/10.14569/IJACSA.2014.050429},
year = {2014},
publisher = {The Science and Information Organization},
volume = {5},
number = {4},
author = {SAMEER M. SHAARAWY and ESAM A. KHAN and MAHMOUD A. ELGAMAL}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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