A Comparative Study on Hybrid Linear and Nonlinear Modeling Framework for Air Passenger Traffic Forecasting

The hybrid linear and nonlinear modeling framework has been widely used as a promising method for time series forecasting. However, there have been very few, if any, large scale comparative studies for the hybrid linear and nonlinear framework for air passenger traffic forecasting. So, we hope this study would fill this gap. The linear models selected are autoregressive integrated moving average model (ARIMA ) and seasonal autoregressive integrated moving average model (SARIMA). As for the nonlinear models, support vector machines (SVMs) and multi-layer feed-forward neural networks (FNN) are selected. Specifically, we employ these models on the four monthly air passenger traffic series of American airlines. The results demonstrate that significant improvement can be achieved with hybrid linear and nonlinear framework, particularly, hybrid framework combined by SARIMA and SVM models performed best in terms of symmetric mean absolute percentage error (SMAPE), multiple comparisons with the best (MCB), and fraction best (FRAC-BEST).

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