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Short-term Forecasting for Airline Industry: The Case of Indian Air Passenger and Air Cargo

Author

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  • Meena Madhavan
  • Mohammed Ali Sharafuddin
  • Pairach Piboonrungroj
  • Ching-Chiao Yang

Abstract

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.

Suggested Citation

  • Meena Madhavan & Mohammed Ali Sharafuddin & Pairach Piboonrungroj & Ching-Chiao Yang, 2023. "Short-term Forecasting for Airline Industry: The Case of Indian Air Passenger and Air Cargo," Global Business Review, International Management Institute, vol. 24(6), pages 1145-1179, December.
    • Handle: RePEc:sae:globus:v:24:y:2023:i:6:p:1145-1179
    DOI: 10.1177/0972150920923316
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    References listed on IDEAS

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    Cited by:

    1. Wandelt, Sebastian & Signori, Andrea & Chang, Shuming & Wang, Shuang & Du, Zhuoming & Sun, Xiaoqian, 2025. "Unleashing the potential of operations research in air transport: A review of applications, methods, and challenges," Journal of Air Transport Management, Elsevier, vol. 124(C).

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