Title: Modelling and forecasting airport passengers: a case study for an introductory forecasting course
Authors: James E. Payne, J'Tia P. Taylor
Addresses: Department of Economics, Illinois State University, Normal, IL 61790-4200, USA. ' Department of Nuclear, Plasma, and Radiological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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.
Keywords: airports; ARIMA; seasonality; static forecasts; dynamic forecasts; airport passengers; case study; introductory forecasting; forecasting courses; time series models; operations management education.
International Journal of Information and Operations Management Education, 2007 Vol.2 No.2, pp.167 - 182
Published online: 01 Oct 2007 *Full-text access for editors Access for subscribers Purchase this article Comment on this article