Abstract
The aims of forecasting are to make decisions. Hence, the selected forecasting method must consider the characteristics of the data under study. Unfortunately, the existent methods proposed in related air transport literature only consider certain characteristics of data. Normally, they consider trend and seasonality, but volatility and distribution are not. However, both factors also contribute to determine the path behavior of data. Clemen (1989) concludes that forecast accuracy can be substantially improved through the combination of multiple individual forecasts. These are the reasons why, in this paper, the hybrid ARIMA + GARCH + Bootstrap time series method is applied for the first time to forecast air transportation passenger demand (pax). This method can combine the trend, variations, and historical distribution of data to eliminate the detrimental effects on forecasting.
