پیش بینی کوتاه مدت مسافر هوایی با استفاده از تجزیه فصلی ترکیبی و روش رگرسیون بردار پشتیبانی حداقل مربعات

In this study, two hybrid approaches based on seasonal decomposition and least squares support vector regression (LSSVR) model are proposed for short-term forecasting of air passenger. In the formulation of the proposed hybrid approaches, the air passenger time series is first decomposed into three components: trend-cycle component, seasonal factor and irregular component. Then the LSSVR model is used to predict the components independently and these prediction results of the components are combined as an aggregated output. Empirical analysis shows that the proposed hybrid approaches are better than other time series models, indicating that they are promising tools to predict complex time series with high volatility and irregularity.