Tourism demand prediction models: theoretical and methodological basis for strengthening the competitiveness of Cuba as a destination

Abstract

Recent changes in tourism have increased the complexity and volatility of tourist demand, especially in the wake of the COVID-19 pandemic. In Cuba, this situation has highlighted the need for tools that can reliably anticipate tourist flows. However, there remains a significant gap in the development, adaptation, and practical application of demand forecasting models tailored to the Cuban context. This limitation reduces the tourism sector's ability to respond in a timely manner to market fluctuations and to support evidence-based strategic decision-making. It is in this context that the present research arises, whose objective is to provide a theoretical basis for the evolution, importance, current situation, and trends in tourism demand forecasting. To develop this study, a review methodology based on the PRISMA model was used, and 681 articles were selected from the Scopus, Dimensions, and Google Scholar databases that met the established criteria. A literature review and descriptive statistics were used. Software such as R-Studio, Excel, Scimago Graphica, and VOSviewer were used for the analysis. The findings indicate an increase in publications since 2014, evidencing a growing research trend. China, Indonesia, India, and the United States stand out as leaders in the production of quality research. In addition, it highlights how tourism demand forecasting is an integral tool for the efficient management of resources in the sector, contributing to economic development and improving the tourist experience.