Publicação
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
| Resumo: | Demand forecast accuracy is critical for hotels to operate their properties efciently and proftably. 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. |
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| Autores principais: | Heo, Cindy Yoonjoung |
| Outros Autores: | Viverit, Luciano; Pereira, Luis |
| Assunto: | Hotel demand forecast Additive pickup Time series COVID-19 pandemic Small and medium-sized enterprises (SMEs) hotels Revenue management |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Algarve |
| Idioma: | inglês |
| Origem: | Sapientia - Universidade do Algarve |
| Resumo: | Demand forecast accuracy is critical for hotels to operate their properties efciently and proftably. 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. |
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