Use of time-series hybrid and non-hybrid models to predict prepare travelers for Baghdad International Airport | ||
| Journal of Administration and Economics | ||
| Article 1, Volume 0, Issue 109, November 2018, Pages 299-319 | ||
| Authors | ||
| O.m.d. Buthayna Abdul Aziz Abdul Jader; Ali Ahmed Hassan | ||
| Abstract | ||
| Prediction in time series is one of the most important topics in statistical procedures and all vital areas it can help managements in the economic and strategic planning and decision-making . This research use time series hybrid models generated from the integration of Box- Jenkins (ARIMA) model and multi-layered neural networks as The first hybrid model (ARIMA-ANN) and second (ANN-ARIMA) model and assuming series includes two components linear and non-linear with the single models such as Box- Jenkins model in which time series is a linear combination , and multi-layered neural networks in which time series has a nonlinear combination to predict the number of passengers in International Baghdad Airport , when comparing these models through a number of statistical criteria ,they show that the hybrid model (ANN-ARIMA) is the superior model because it has lower values of these criteria , and it has been used to calculate the prediction of passenger numbers for the time period from September 2015 until December 2016 , where the data were analyzed and the results were obtained based on the statistical programs package (Minitab 16 , SPSS 19) . | ||
| Keywords | ||
| Statistical axis | ||
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