| 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 | |
| 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. |
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