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

Cindy Yoonjoung Heo (), Luciano Viverit () and Luís Nobre Pereira ()
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Cindy Yoonjoung Heo: HES-SO/ University of Applied Sciences and Art Western Switzerland
Luciano Viverit: Hotelnet
Luís Nobre Pereira: Universidade do Algarve

Journal of Revenue and Pricing Management, 2024, vol. 23, issue 1, No 5, 39-43

Abstract: 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.

Keywords: Hotel demand forecast; Additive pickup; Time series; COVID-19 pandemic; Small and medium-sized enterprises (SMEs) hotels; Revenue management (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1057/s41272-023-00421-1

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