Evaluating machine learning techniques for predicting tourist occupancy: an experiment with pre- and post-pandemic COVID-19 data

L. Moreno-Izquierdo, A. Más-Ferrando, J. F. Perles-Ribes, A. Rubia-Serrano and T. Torregrosa-Martí
Authors registered in the RePEc Author Service: José Francisco Perles Ribes

Current Issues in Tourism, 2024, vol. 27, issue 22, 3754-3769

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.

Date: 2024
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DOI: 10.1080/13683500.2023.2282163

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