IDEAS home Printed from https://ideas.repec.org/a/eee/jaitra/v133y2026ics0969699726000177.html

Beyond route-specific forecasting: An empirical test of two cross-series transfer learning strategies for airline demand with short-data constraints

Author

Listed:
  • Lee, Kiljae K.
  • Abdelghany, Ahmed F.

Abstract

Airline demand forecasting frequently faces a “short-data†problem, where individual routes have limited historical records due to new route launches, seasonal suspensions, or external shocks such as COVID-19. While this constraint impedes the performance of forecasting models trained on single-series data, the presence of numerous parallel routes—a typical characteristic of airline networks—presents a “short-but-wide†data structure, offering a clear opportunity for cross-series transfer learning.

Suggested Citation

  • Lee, Kiljae K. & Abdelghany, Ahmed F., 2026. "Beyond route-specific forecasting: An empirical test of two cross-series transfer learning strategies for airline demand with short-data constraints," Journal of Air Transport Management, Elsevier, vol. 133(C).
    • Handle: RePEc:eee:jaitra:v:133:y:2026:i:c:s0969699726000177
    DOI: 10.1016/j.jairtraman.2026.102981
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0969699726000177
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jairtraman.2026.102981?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
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Smyl, Slawek, 2020. "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting," International Journal of Forecasting, Elsevier, vol. 36(1), pages 75-85.
    2. Matthew J. Kotchen & James H. Stock & Catherine D. Wolfram, 2019. "Introduction to "Environmental and Energy Policy and the Economy"," NBER Chapters, in: Environmental and Energy Policy and the Economy, volume 1, pages 3-7, National Bureau of Economic Research, Inc.
    3. Tuğba Dayioglu & Serdar Alnipak, 2023. "Dynamic effecting factors of air travel demand: an econometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3713-3727, August.
    4. Tsui, Wai Hong Kan & Ozer Balli, Hatice & Gilbey, Andrew & Gow, Hamish, 2014. "Forecasting of Hong Kong airport's passenger throughput," Tourism Management, Elsevier, vol. 42(C), pages 62-76.
    5. Montero-Manso, Pablo & Hyndman, Rob J., 2021. "Principles and algorithms for forecasting groups of time series: Locality and globality," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1632-1653.
    6. Bhadra, Dipasis, 2004. "Air Travel in Small Communities: An Econometric Framework and Results," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 43(01).
    7. Jin, Feng & Li, Yongwu & Sun, Shaolong & Li, Hongtao, 2020. "Forecasting air passenger demand with a new hybrid ensemble approach," Journal of Air Transport Management, Elsevier, vol. 83(C).
    8. Abdelghany, Ahmed & Guzhva, Vitaly S., 2010. "A time-series modelling approach for airport short-term demand forecasting," Journal of Airport Management, Henry Stewart Publications, vol. 5(1), pages 72-87, September.
    9. Suh, Daniel Y. & Ryerson, Megan S., 2019. "Forecast to grow: Aviation demand forecasting in an era of demand uncertainty and optimism bias," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 400-416.
    10. Hopfe, David H. & Lee, Kiljae & Yu, Chunyan, 2024. "Short-term forecasting airport passenger flow during periods of volatility: Comparative investigation of time series vs. neural network models," Journal of Air Transport Management, Elsevier, vol. 115(C).
    11. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    12. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
    13. Kasun Bandara, 2023. "Forecasting with Big Data Using Global Forecasting Models," Palgrave Advances in Economics of Innovation and Technology, in: Mohsen Hamoudia & Spyros Makridakis & Evangelos Spiliotis (ed.), Forecasting with Artificial Intelligence, chapter 0, pages 107-122, Palgrave Macmillan.
    14. Evangelos Spiliotis, 2023. "Time Series Forecasting with Statistical, Machine Learning, and Deep Learning Methods: Past, Present, and Future," Palgrave Advances in Economics of Innovation and Technology, in: Mohsen Hamoudia & Spyros Makridakis & Evangelos Spiliotis (ed.), Forecasting with Artificial Intelligence, chapter 0, pages 49-75, Palgrave Macmillan.
    15. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    16. Grubb, Howard & Mason, Alexina, 2001. "Long lead-time forecasting of UK air passengers by Holt-Winters methods with damped trend," International Journal of Forecasting, Elsevier, vol. 17(1), pages 71-82.
    17. 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.
    18. Shaohui Ma & Robert Fildes, 2023. "Large-Scale Time Series Forecasting with Meta-Learning," Palgrave Advances in Economics of Innovation and Technology, in: Mohsen Hamoudia & Spyros Makridakis & Evangelos Spiliotis (ed.), Forecasting with Artificial Intelligence, chapter 0, pages 221-250, Palgrave Macmillan.
    19. Lim, Bryan & Arık, Sercan Ö. & Loeff, Nicolas & Pfister, Tomas, 2021. "Temporal Fusion Transformers for interpretable multi-horizon time series forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1748-1764.
    20. 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).
    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. Abdelghany, Ahmed & Abdelghany, Khaled & Guzhva, Vitaly S. & Kai, Mary, 2025. "Unraveling endogeneity in seat capacity and Fares: Time series econometric models for airline origin-destination passengers forecasting," Journal of Air Transport Management, Elsevier, vol. 128(C).
    2. Wellens, Arnoud P. & Boute, Robert N. & Udenio, Maximiliano, 2024. "Simplifying tree-based methods for retail sales forecasting with explanatory variables," European Journal of Operational Research, Elsevier, vol. 314(2), pages 523-539.
    3. Hopfe, David H. & Lee, Kiljae & Yu, Chunyan, 2024. "Short-term forecasting airport passenger flow during periods of volatility: Comparative investigation of time series vs. neural network models," Journal of Air Transport Management, Elsevier, vol. 115(C).
    4. Semenoglou, Artemios-Anargyros & Spiliotis, Evangelos & Makridakis, Spyros & Assimakopoulos, Vassilios, 2021. "Investigating the accuracy of cross-learning time series forecasting methods," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1072-1084.
    5. Wenhui Zhao & Tong Li & Danyang Xu & Zhaohua Wang, 2024. "A global forecasting method of heterogeneous household short-term load based on pre-trained autoencoder and deep-LSTM model," Annals of Operations Research, Springer, vol. 339(1), pages 227-259, August.
    6. Gunter, Ulrich & Zekan, Bozana, 2021. "Forecasting air passenger numbers with a GVAR model," Annals of Tourism Research, Elsevier, vol. 89(C).
    7. Feddersen, Leif & Cleophas, Catherine, 2026. "Hierarchical neural additive models for interpretable demand forecasts," International Journal of Forecasting, Elsevier, vol. 42(1), pages 216-234.
    8. Bojer, Casper Solheim, 2022. "Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1555-1561.
    9. Tuominen, Jalmari & Pulkkinen, Eetu & Peltonen, Jaakko & Kanniainen, Juho & Oksala, Niku & Palomäki, Ari & Roine, Antti, 2024. "Forecasting emergency department occupancy with advanced machine learning models and multivariable input," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1410-1420.
    10. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    11. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "Predicting/hypothesizing the findings of the M5 competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1337-1345.
    12. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    13. Tiantian Tu, 2025. "Bridging Short- and Long-Term Dependencies: A CNN-Transformer Hybrid for Financial Time Series Forecasting," Papers 2504.19309, arXiv.org.
    14. 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).
    15. Ari, Didem & Mizrak Ozfirat, Pinar, 2024. "Comparison of artificial neural networks and regression analysis for airway passenger estimation," Journal of Air Transport Management, Elsevier, vol. 115(C).
    16. Abdelfatah, Omar Sharafeldin Mohamed, 2026. "AI-Driven Demand Forecasting and Its Impact on Inventory Optimization," SocArXiv uw57j_v1, Center for Open Science.
    17. Neubauer, Lukas & Filzmoser, Peter, 2024. "Improving forecasts for heterogeneous time series by “averaging”, with application to food demand forecasts," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1622-1645.
    18. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "M5 accuracy competition: Results, findings, and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1346-1364.
    19. Zhang, Hanyu & Zandehshahvar, Reza & Tanneau, Mathieu & Van Hentenryck, Pascal, 2025. "Weather-informed probabilistic forecasting and scenario generation in power systems," Applied Energy, Elsevier, vol. 384(C).
    20. Xu, Shilin & Liu, Yang & Jin, Chun, 2023. "Forecasting daily tourism demand with multiple factors," Annals of Tourism Research, Elsevier, vol. 103(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:jaitra:v:133:y:2026:i:c:s0969699726000177. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/journal-of-air-transport-management/ .

    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.