Forecasting Air Passenger Traffic Flows in Canada: An Evaluation of Time Series Models and Combination Methods

This master’s thesis studies the Canadian air transportation sector, which has experienced significant growth over the past fifteen years. It provides short and medium term forecasts of the number of enplaned/deplaned air passengers in Canada for three geographical subdivisions of the market: domestic, transborder (US) and international flights. It uses various time series forecasting models: harmonic regression, Holt-Winters exponential smoothing, autoregressive-integrated-moving average (ARIMA) and seasonal autoregressive-integrated-moving average (SARIMA) regressions. In addition, it examines whether or not combining forecasts from each single model helps to improve forecasting accuracy. This last part of the study is done by applying two forecasting combination techniques: simple averaging and a variety of variance-covariance methods. Our results indicate that all models provide accurate forecasts, with MAPE and RMSPE scores below 10% on average. All adequately capture the main statistical characteristics of the Canadian air passenger series. Furthermore, combined forecasts from the single models always outperform those obtained from the single worst model. In some instances, they even dominate the forecasts from the single best model. Finally, these results should encourage the Canadian government, air transport authorities, and the airlines operating in Canada to use combination techniques to improve their short and medium term forecasts of passenger flows.