Forecasting Air Passengers of Changi Airport Based on Seasonal Decomposition and an LSSVM Model

This work aimed to determine a suitable method to provide air traffic passenger forecasts of Changi airport. A linear forecasting technique in the form of a seasonal autoregressive integrated moving average (SARIMA) model and a nonlinear technique known as the least squares support vector machine (LSSVM) were compared. A hybrid X-13 LSSVM approach was also compared. A fourth approach was proposed to leverage the outputs of the hybrid X-13 LSSVM method to conduct forecasts for longer forecasting horizons. Results showed that SARIMA, direct LSSVM and X-13 LSSVM methods were able to provide accurate 1-month-ahead forecasts. However, SARIMA and direct LSSVM methods both suffered from forecasting inaccuracy, as the forecasting horizon increased. The X-13 LSSVM outperformed both SARIMA and direct LSSVM methods, in terms of small magnitude errors and forecasting directional changes across the forecasting horizons. The proposed fourth approach was able to provide 24-months-ahead forecasts and was easy to implement.