The application of the ARIMA model for time series air freight forecasting

In recent years, air freight has attracted vigorous attention among scholars due to its continuous growth and importance in decision making. Developing an accurate forecast for the air cargo market is essential for empowering planning processes and providing guidance for the air cargo industry's key stakeholders. Nevertheless, only a few research papers have been developed to tackle this topic. Hence, this study is devoted to applying the automated algorithm from the autoregressive integrated moving average (ARIMA) modelling to predict time series data of air cargo outbound in eight geographical regions. The experimental findings show good performance of the selected models to be used for accurate predictions. The goodness of fit of the candidates' models is assessed based on different statistical key indicators. The results provide a useful prediction basis for the air cargo market and emphasise the future performance of air freight over the next years.