Forecasting the Number of Muslim Pilgrims Using NARX Neural Networks with a Comparison Study with Other Modern Methods
Esam A. Khan
The Custodian of the Two Holy Mosques Institute for Hajj and Omra Research, Umm Al -Qura University, Makkah, Saudi Arabia.
Mahmoud A. Elgamal *
The Custodian of the Two Holy Mosques Institute for Hajj and Omra Research, Umm Al -Qura University, Makkah, Saudi Arabia.
Sameer M. Shaarawy
The Custodian of the Two Holy Mosques Institute for Hajj and Omra Research, Umm Al -Qura University, Makkah, Saudi Arabia.
*Author to whom correspondence should be addressed.
Abstract
Pilgrimage (Hajj) of Muslims is considered the largest human gathering all over the world in which more than three millions move together through a very limited space in a short time period. The yearly number of pilgrims coming from outside Saudi Arabia, denoted by NPO for short, is more than two thirds of the total number of Pilgrims. Therefore forecasting the NPO is considered by Saudi Arabia as the most important indicator in determining the planning mechanism for future secure and comfortable hajj seasons. The main objective of this article is to employ the NARX neural networks to forecast the yearly series of NPO and to show that it gives better forecasts than Box–Jenkins and Bayesian Procedures. In order to achieve our objective, the NARX is used to forecast the future five observations and the results are compared with the results given in [1].
Keywords: Forecast, NARX, Bayesian analysis, box and Jenkins methodology, Mean Average Percentage Error (MAPE)