Forecasting revenue passenger enplanements using wavelet-support vector machine

Abstract

Forecasting is an important element in an airline industry due to its capability in projecting airport activities that will reflect the relationship that drives aviation activities. A wavelet-support vector machine (WSVM) conjunction model for revenue passenger enplanements forecast is proposed in this study. The conjunction model is the combination of two models which are Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM). The method is then compared with single SVM and Seasonal Decomposition-Support Vector Machine (SDSVM) conjunctions. Seasonal Decomposition (SD) readings are obtained through X-12- ARIMA. The monthly domestic and international revenue passenger enplanements data dated from January 1996 to December 2012 are used. The performances of the three models are then compared utilizing mean absolute error (MAE), mean square error (MSE) and mean absolute percentage error (MAPE). The results indicate that WSVM conjunction model has higher accuracy and performs better than both basic single SVM and SDSVM conjunctions.