Forecasting of air traffic means to estimate the number of prospective passengers that use air transport. Time Series data for the number of passengers in Saudi Arabian Airlines are collected in Gregorian and partially in Hijri calendars. The data are recorded monthly for ten years from January 2007G to July 2017G in Gregorian. However, Hijri data were only from Muharram 1435H to Shawal 1438H. The Gregorian data are transformed to Hijri to complete the missing gab. A major question to be studied is raised about existence of seasonality and which data is better than the other in expressing seasonality pattern. Another important question is to identify months of the year having better seasonality than others. To explore the seasonality patterns for the time series data in Gregorian and Hijri calendars we used the following methods: Scatter Diagrams, Autocorrelation Functions, x2Goodness-of-Fit Test, Seasonality Indexes using Ratio to Moving Average, Plot of Changes in the Seasonal Pattern and Autoregression to assess strength of seasonality. New methods are proposed to assess the seasonality of the data and moreover to identify which months of the year have better seasonality, these methods are: Month’s Orders, Measures of Dispersion for Seasonality Indexes (Range, Quartile Deviation, Average Deviation and Standard Deviation).
