IDEAS home Printed from https://ideas.repec.org/a/sae/globus/v24y2023i6p1145-1179.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0972150920923316
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0972150920923316?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jia Liu & Daniel J. Nordman & William Q. Meeker, 2016. "The Number of MCMC Draws Needed to Compute Bayesian Credible Bounds," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 275-284, July.
    2. Marazzo, Marcial & Scherre, Rafael & Fernandes, Elton, 2010. "Air transport demand and economic growth in Brazil: A time series analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(2), pages 261-269, March.
    3. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    4. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    5. Xu, Shuojiang & Chan, Hing Kai & Zhang, Tiantian, 2019. "Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 169-180.
    6. Shu-Chuan Chen & Shih-Yao Kuo & Kuo-Wei Chang & Yi-Ting Wang, 2012. "Improving the forecasting accuracy of air passenger and air cargo demand: the application of back-propagation neural networks," Transportation Planning and Technology, Taylor & Francis Journals, vol. 35(3), pages 373-392, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gunter, Ulrich & Zekan, Bozana, 2021. "Forecasting air passenger numbers with a GVAR model," Annals of Tourism Research, Elsevier, vol. 89(C).
    2. Wang, Sen & Gao, Yi, 2021. "A literature review and citation analyses of air travel demand studies published between 2010 and 2020," Journal of Air Transport Management, Elsevier, vol. 97(C).
    3. Emami Javanmard, Majid & Tang, Yili & Martínez-Hernández, J. Adrián, 2024. "Forecasting air transportation demand and its impacts on energy consumption and emission," Applied Energy, Elsevier, vol. 364(C).
    4. Gudmundsson, S.V. & Cattaneo, M. & Redondi, R., 2021. "Forecasting temporal world recovery in air transport markets in the presence of large economic shocks: The case of COVID-19," Journal of Air Transport Management, Elsevier, vol. 91(C).
    5. Khan, Muhammad Asif & Segovia, Juan E.Trinidad & Bhatti, M.Ishaq & Kabir, Asif, 2023. "Corporate vulnerability in the US and China during COVID-19: A machine learning approach," The Journal of Economic Asymmetries, Elsevier, vol. 27(C).
    6. Daniel Ştefan Armeanu & Georgeta Vintilă & Ştefan Cristian Gherghina, 2017. "Empirical Study towards the Drivers of Sustainable Economic Growth in EU-28 Countries," Sustainability, MDPI, vol. 10(1), pages 1-22, December.
    7. Rayaprolu, Hema & Levinson, David, 2024. "Co-evolution of public transport access and ridership," Journal of Transport Geography, Elsevier, vol. 116(C).
    8. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    9. Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 6(53), pages 286-303, January.
    10. Patrick Bajari & Victor Chernozhukov & Ali Hortaçsu & Junichi Suzuki, 2019. "The Impact of Big Data on Firm Performance: An Empirical Investigation," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 33-37, May.
    11. Katsuyuki Tanaka & Takuo Higashide & Takuji Kinkyo & Shigeyuki Hamori, 2025. "A Multi-Stage Financial Distress Early Warning System: Analyzing Corporate Insolvency with Random Forest," JRFM, MDPI, vol. 18(4), pages 1-16, April.
    12. Dombi, József & Jónás, Tamás & Tóth, Zsuzsanna Eszter, 2018. "Modeling and long-term forecasting demand in spare parts logistics businesses," International Journal of Production Economics, Elsevier, vol. 201(C), pages 1-17.
    13. Amita Gajewar & Gagan Bansal, 2016. "Revenue Forecasting for Enterprise Products," Papers 1701.06624, arXiv.org.
    14. Tao XIONG & Chongguang LI & Yukun BAO, 2017. "An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(3), pages 136-148.
    15. Pieter van der Spek & Chris Verhoef, 2014. "Balancing Time‐to‐Market and Quality in Embedded Systems," Systems Engineering, John Wiley & Sons, vol. 17(2), pages 166-192, June.
    16. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    17. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
    18. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    19. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    20. Crespo, Cristian, 2020. "Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout," LSE Research Online Documents on Economics 123139, London School of Economics and Political Science, LSE Library.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:globus:v:24:y:2023:i:6:p:1145-1179. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: http://www.imi.edu/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.