Reduce Unexpected Aırlıne Dıverts: Modellıng wıth Tıme Serıes Analysıs

In this study, a decision support system was designed to minimize the costs caused by an airline's unexpected diverts. Meteorological data provided by an airline was used to predict visibility range, using the R programming language. The results of the analyzes are presented. It is aimed to make predictions by analyzing the data using time series analysis methods. Detailed forecasts were made to correspond to 3 forward-looking hours. The results obtained from time series analysis using AR, MA, ARMA, ARIMA, AutoARIMA and VAR were compared according to the error rate functions.