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Forecasting Hotel Occupancy Rates With Artificial Neural Networks in the COVID-19 Process

Forecasting Hotel Occupancy Rates With Artificial Neural Networks in the COVID-19 Process

Arzu Organ, Cansu Tosun Gavcar
Copyright: © 2021 |Pages: 20
ISBN13: 9781799882312|ISBN10: 1799882314|ISBN13 Softcover: 9781799882329|EISBN13: 9781799882336
DOI: 10.4018/978-1-7998-8231-2.ch028
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MLA

Organ, Arzu, and Cansu Tosun Gavcar. "Forecasting Hotel Occupancy Rates With Artificial Neural Networks in the COVID-19 Process." Handbook of Research on the Impacts and Implications of COVID-19 on the Tourism Industry, edited by Mahmut Demir, et al., IGI Global, 2021, pp. 583-602. https://doi.org/10.4018/978-1-7998-8231-2.ch028

APA

Organ, A. & Tosun Gavcar, C. (2021). Forecasting Hotel Occupancy Rates With Artificial Neural Networks in the COVID-19 Process. In M. Demir, A. Dalgıç, & F. Ergen (Eds.), Handbook of Research on the Impacts and Implications of COVID-19 on the Tourism Industry (pp. 583-602). IGI Global. https://doi.org/10.4018/978-1-7998-8231-2.ch028

Chicago

Organ, Arzu, and Cansu Tosun Gavcar. "Forecasting Hotel Occupancy Rates With Artificial Neural Networks in the COVID-19 Process." In Handbook of Research on the Impacts and Implications of COVID-19 on the Tourism Industry, edited by Mahmut Demir, Ali Dalgıç, and Fatma Doğanay Ergen, 583-602. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-8231-2.ch028

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Abstract

In the tourism sector, accommodation business demand forecasting provides a great benefit for tourism professionals, especially hotel managers, in the strategic decision-making process. For demand estimation, the artificial neural networks (ANN) method, which works similar to a human brain cell and makes realistic predictions, has been preferred. The aim of this study was to develop an eight input and output variable of the feedforward radiated back an ANN is in a specially certified hotel room occupancy rate in Turkey to investigate the applicability of the method to predict. Four different alternative network structures were created from the data set with the K-fold cross validation method. As a result of the test simulation, it was determined that the estimated and actual occupancy rates of the network with the lowest error were close to each other. According to this designed model, the monthly occupancy rate for the years 2019 and 2020 has been estimated. As a result, the effect of COVID-19 was revealed by comparing the hotel occupancy rate with the actual rates.

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