Modelling and forecasting airport passengers: a case study for an introductory forecasting course
by James E. Payne, J'Tia P. Taylor
International Journal of Information and Operations Management Education (IJIOME), Vol. 2, No. 2, 2007

Abstract: This case study utilises monthly data on the total number of airport passengers from the Central Illinois Regional Airport to illustrate the steps required to construct and estimate two low-cost time series models for use in generating in-sample and out-of-sample forecasts: an Autoregressive Integrated Moving Average (ARIMA) model and an autoregressive-seasonal-trend model. The case highlights the importance of the various stages in the development of the respective models: identification, estimation and residual diagnostics. The results of the case study reveals that the autoregressive-seasonal-trend model outperforms the ARIMA model with respect to both in-sample and out-of-sample forecasting performance.

Online publication date: Mon, 01-Oct-2007

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