Short-term Forecasting for Airline Industry: The Case of Indian Air Passenger and Air Cargo
This study aims to forecast air passenger and cargo demand of the Indian aviation industry using the autoregressive integrated moving average (ARIMA) and Bayesian structural time series (BSTS) models. We utilized 10 years’ (2009–2018) air passenger and cargo data obtained from the Directorate Genera...
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| Veröffentlicht in: | Global business review 2023-12, Vol.24 (6), p.1145-1179 |
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| creator | Madhavan, Meena Ali Sharafuddin, Mohammed Piboonrungroj, Pairach Yang, Ching-Chiao |
| description | This study aims to forecast air passenger and cargo demand of the Indian aviation industry using the autoregressive integrated moving average (ARIMA) and Bayesian structural time series (BSTS) models. We utilized 10 years’ (2009–2018) air passenger and cargo data obtained from the Directorate General of Civil Aviation (DGCA-India) website. The study assessed both ARIMA and BSTS models’ ability to incorporate uncertainty under dynamic settings. Findings inferred that, along with ARIMA, BSTS is also suitable for short-term forecasting of all four (international passenger, domestic passenger, international air cargo, and domestic air cargo) commercial aviation sectors. Recommendations and directions for further research in medium-term and long-term forecasting of the Indian airline industry were also summarized. |
| doi_str_mv | 10.1177/0972150920923316 |
| format | Article |
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| ispartof | Global business review, 2023-12, Vol.24 (6), p.1145-1179 |
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| language | eng |
| recordid | cdi_proquest_journals_2894331946 |
| source | PAIS Index; SAGE Complete A-Z List |
| subjects | Air travel Aviation Forecasting Short term |
| title | Short-term Forecasting for Airline Industry: The Case of Indian Air Passenger and Air Cargo |
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