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.
| Original language | English |
|---|---|
| Pages (from-to) | 169-180 |
| Number of pages | 12 |
| Journal | Transportation Research Part E: Logistics and Transportation Review |
| Volume | 122 |
| DOIs | |
| Publication status | Published - Feb 2019 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 11 Sustainable Cities and Communities
Free Keywords
- Aviation industry
- Gaussian white noise
- SARIMA
- SVR
- Time series forecasting
ASJC Scopus subject areas
- Business and International Management
- Civil and Structural Engineering
- Transportation
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