| Title |
Evaluating machine learning techniques for predicting tourist occupancy: an experiment with pre- and post-pandemic COVID-19 data |
| ID_Doc |
61872 |
| Authors |
Moreno-Izquierdo, L; Más-Ferrando, A; Perles-Ribes, JF; Rubia-Serrano, A; Torregrosa-Marti, T |
| Title |
Evaluating machine learning techniques for predicting tourist occupancy: an experiment with pre- and post-pandemic COVID-19 data |
| Year |
2023 |
| Published |
|
| DOI |
10.1080/13683500.2023.2282163 |
| Abstract |
This paper analyses the prediction capacity of machine learning techniques under severe demand shocks. Specifically, three methods - Naive Bayes, Random Forest and Support Vector Machine - are tested in predicting rental occupancy for tourist accommodation in the city of Madrid. We compare two different scenarios: firstly, the predictive capacity in the years prior to COVID-19 and, secondly, the ability to anticipate demand behaviour once the pandemic started. The results demonstrate first that without market disturbances, the Random Forest model exhibits the best predictive capability. Second, the COVID-19 pandemic caused such major changes that none of the three tested models are entirely reliable, although the Random Forest and Naive Bayes models outperform the SVM model. As a methodological novelty, this paper includes occupancy quantiles to resolve problems with available data and temporal biases. |
| Author Keywords |
Tourist occupancy; Airbnb; prediction; tourist demand; machine learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Social Science Citation Index (SSCI) |
| EID |
WOS:001100505800001 |
| WoS Category |
Hospitality, Leisure, Sport & Tourism |
| Research Area |
Social Sciences - Other Topics |
| PDF |
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