Estudio Longitudinal de la estacionalidad turísticas en España usando Redes Neuronales

Abstract

Spain has become a benchmark in terms of tourism, since the number of visitors has multiplied extraordinarily, and has gone from a uniform tourism model, throughout the peninsula, to a nuclear model. The economic variations associated with the economic cycle associated with tourism have an indisputable economic impact on autonomous communities. In this work, the seasonality of tourists in pre-pandemic periods was studied. For this purpose, artificial neural networks were used to predict the type of tourist associated with summer periods. Hence, the artificial neural network was used as a technique that achieved an 86.90% accuracy rate. The relevant results showed that domestic tourists had a greater share of seasonality throughout the year than foreign tourists, while foreigners are more significant in summer periods.