Short-term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches |
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| Institution: | 1. Center for Forecasting Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS), Beijing 100190, China;2. Department of Management Sciences, City University of Hong Kong, Hong Kong, China;1. Department of Shipping and Transportation Management, National Taiwan Ocean University, #2 Pei-Ning Road, Keelung 202, Taiwan, R.O.C;2. Department of Route Planning, TransAsia Airways 9F, #139, Cheng-Chou, Road, Taipei 103, Taiwan, R.O.C;1. Institute of Systems Engineering and Control School of Traffic and Transport, Beijing Jiaotong University, Beijing 100044, PR China;2. Beijing Transport Management Technical Support Center, Beijing 100055, PR China;3. School of Computer Science & Engineering, Beihang University, Beijing 100191, PR China |
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| Abstract: | 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. |
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| Keywords: | Hybrid approach Short-term forecasting Air passenger Seasonal decomposition Least squares support vector regression |
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