IDEAS home Printed from https://ideas.repec.org/a/pal/jorapm/v23y2024i1d10.1057_s41272-023-00421-1.html
   My bibliography  Save this article

Does historical data still matter for demand forecasting in uncertain and turbulent times? An extension of the additive pickup time series method for SME hotels

Author

Listed:
  • Cindy Yoonjoung Heo

    (HES-SO/ University of Applied Sciences and Art Western Switzerland)

  • Luciano Viverit

    (Hotelnet)

  • Luís Nobre Pereira

    (Universidade do Algarve)

Abstract

Demand forecast accuracy is critical for hotels to operate their properties efficiently and profitably. The COVID-19 pandemic is a massive challenge for hotel demand forecasting due to the relevance of historical data. Therefore, the aims of this study are twofold: to present an extension of the additive pickup method using time series and moving averages; and to test the model using the real reservation data of a hotel in Italy during the COVID-19 pandemic. This study shows that historical data are still useful for a SME hotel amid substantial demand uncertainty caused by COVID-19. Empirical results suggest that the proposed method performs better than the classical one, particularly for longer forecasting horizons and for periods when the hotel is not fully occupied.

Suggested Citation

  • Cindy Yoonjoung Heo & Luciano Viverit & Luís Nobre Pereira, 2024. "Does historical data still matter for demand forecasting in uncertain and turbulent times? An extension of the additive pickup time series method for SME hotels," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(1), pages 39-43, February.
    • Handle: RePEc:pal:jorapm:v:23:y:2024:i:1:d:10.1057_s41272-023-00421-1
    DOI: 10.1057/s41272-023-00421-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41272-023-00421-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1057/s41272-023-00421-1?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Hanyuan Zhang & Jiangping Lu, 2022. "Forecasting hotel room demand amid COVID-19," Tourism Economics, , vol. 28(1), pages 200-221, February.
    2. Kourentzes, Nikolaos & Saayman, Andrea & Jean-Pierre, Philippe & Provenzano, Davide & Sahli, Mondher & Seetaram, Neelu & Volo, Serena, 2021. "Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team," Annals of Tourism Research, Elsevier, vol. 88(C).
    3. Weatherford, Larry R. & Kimes, Sheryl E., 2003. "A comparison of forecasting methods for hotel revenue management," International Journal of Forecasting, Elsevier, vol. 19(3), pages 401-415.
    4. Anna Maria Fiori & Ilaria Foroni, 2019. "Reservation Forecasting Models for Hospitality SMEs with a View to Enhance Their Economic Sustainability," Sustainability, MDPI, vol. 11(5), pages 1-24, February.
    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. Ulrich Gunter, 2021. "Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests," Forecasting, MDPI, vol. 3(4), pages 1-36, November.
    2. Hanyuan Zhang & Jiangping Lu, 2022. "Forecasting hotel room demand amid COVID-19," Tourism Economics, , vol. 28(1), pages 200-221, February.
    3. Tianxiang Zheng & Shaopeng Liu & Zini Chen & Yuhan Qiao & Rob Law, 2020. "Forecasting Daily Room Rates on the Basis of an LSTM Model in Difficult Times of Hong Kong: Evidence from Online Distribution Channels on the Hotel Industry," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    4. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    5. Doris Chenguang Wu & Shiteng Zhong & Richard T R Qiu & Ji Wu, 2022. "Are customer reviews just reviews? Hotel forecasting using sentiment analysis," Tourism Economics, , vol. 28(3), pages 795-816, May.
    6. Binru Zhang & Yulian Pu & Yuanyuan Wang & Jueyou Li, 2019. "Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index," Sustainability, MDPI, vol. 11(17), pages 1-14, August.
    7. Guizzardi, Andrea & Pons, Flavio Maria Emanuele & Angelini, Giovanni & Ranieri, Ercolino, 2021. "Big data from dynamic pricing: A smart approach to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1049-1060.
    8. Valerio Lacagnina & Davide Provenzano, 2016. "An integrated fuzzy-stochastic model for revenue management," Tourism Economics, , vol. 22(4), pages 779-792, August.
    9. Haensel, Alwin & Koole, Ger, 2011. "Booking horizon forecasting with dynamic updating: A case study of hotel reservation data," International Journal of Forecasting, Elsevier, vol. 27(3), pages 942-960, July.
    10. Pritularga, Kandrika F. & Svetunkov, Ivan & Kourentzes, Nikolaos, 2021. "Stochastic coherency in forecast reconciliation," International Journal of Production Economics, Elsevier, vol. 240(C).
    11. Giovanni De Luca & Monica Rosciano, 2020. "Quantile Dependence in Tourism Demand Time Series: Evidence in the Southern Italy Market," Sustainability, MDPI, vol. 12(8), pages 1-18, April.
    12. Guizzardi, Andrea & Ballestra, Luca Vincenzo & D'Innocenzo, Enzo, 2022. "Hotel dynamic pricing, stochastic demand and covid-19," Annals of Tourism Research, Elsevier, vol. 97(C).
    13. Xi Wu & Adam Blake, 2023. "The Impact of the COVID-19 Crisis on Air Travel Demand: Some Evidence From China," SAGE Open, , vol. 13(1), pages 21582440231, January.
    14. Marvello Yang & Norizan Jaafar & Abdullah Al Mamun & Anas A. Salameh & Noorshella Che Nawi, 2022. "Modelling the significance of strategic orientation for competitive advantage and economic sustainability: the use of hybrid SEM–neural network analysis," Journal of Innovation and Entrepreneurship, Springer, vol. 11(1), pages 1-28, December.
    15. Davide Provenzano & Serena Volo, 2022. "Tourism recovery amid COVID-19: The case of Lombardy, Italy," Tourism Economics, , vol. 28(1), pages 110-130, February.
    16. Anna Maria Fiori & Ilaria Foroni, 2019. "Reservation Forecasting Models for Hospitality SMEs with a View to Enhance Their Economic Sustainability," Sustainability, MDPI, vol. 11(5), pages 1-24, February.
    17. George Athanasopoulos & Nikolaos Kourentzes, 2021. "On the Evaluation of Hierarchical Forecasts," Monash Econometrics and Business Statistics Working Papers 10/21, Monash University, Department of Econometrics and Business Statistics.
    18. Diego A. B. Marconatto & Gaspar A. Peixoto & Emidio G. Teixeira & Adelar Fochezatto, 2022. "Women on the Front Line: The Growth of SMEs during Crises," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    19. Trinh, Vu Quang & Seetaram, Neelu, 2022. "Top-management compensation and survival likelihood: the case of tourism and leisure firms in the US," Annals of Tourism Research, Elsevier, vol. 92(C).
    20. Timothy Webb, 2016. "From travel agents to OTAs: How the evolution of consumer booking behavior has affected revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(3), pages 276-282, July.

    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:pal:jorapm:v:23:y:2024:i:1:d:10.1057_s41272-023-00421-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave.com .

    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.