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Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach

Shuojiang Xu, Hing Kai Chan and Tiantian Zhang

Transportation Research Part E: Logistics and Transportation Review, 2019, vol. 122, issue C, 169-180

Abstract: In this study, a novel SARIMA-SVR model is proposed to forecast statistical indicators in the aviation industry that can be used for later capacity management and planning purpose. First, the time series is analysed by SARIMA. Then, Gaussian White Noise is reversely calculated. Next, four hybrid models are proposed and applied to forecast the future statistical indicators in the aviation industry. The results of empirical study suggest that one of the proposed models, namely SARIMA_SVR3, can achieve better accuracy than other methods, and prove that incorporating Gaussian White Noise is able to increase forecasting accuracy.

Keywords: Aviation industry; SARIMA; SVR; Gaussian white noise; Time series forecasting (search for similar items in EconPapers)
Date: 2019
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Handle: RePEc:eee:transe:v:122:y:2019:i:c:p:169-180