Tourism Growth Prediction Based on Deep Learning Approach

Xiaoling Ren, Yanyan Li, JuanJuan Zhao, Yan Qiang and M. Irfan Uddin

Complexity, 2021, vol. 2021, 1-10

Abstract: The conventional tourism demand prediction models are currently facing several challenges due to the excess number of search intensity indices that are used as indicators of tourism demand. In this work, the framework for deep learning-based monthly prediction of the volumes of Macau tourist arrivals was presented. The main objective in this study is to predict the tourism growth via one of the deep learning algorithms of extracting new features. The outcome of this study showed that the performance of the adopted deep learning framework was better than that of artificial neural network and support vector regression models. Practitioners can rely on the identified relevant features from the developed framework to understand the nature of the relationships between the predictive factors of tourist demand and the actual volume of tourist arrival.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:5531754

DOI: 10.1155/2021/5531754

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