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.
Highlights
Most countries depend on the tourism sector for economic growth as it creates jobs and contribute about 10.4% to the global gross domestic product (GDP) as at 2019 [1]
Both quantitative and qualitative techniques are used in tourism demand (TD) forecasting. e qualitative techniques depend on intuition, experience, and understanding of a specific destination market and exhibit poor adaptability [5]. ey are used mainly in the prediction of tourist arrivals based on historical records and other determinants of tourism volume [6]
Deep learning is an aspect of artificial intelligence that is considered a potential alternative to the existing TD prediction models due to its two unique properties which are (i) ability to naturally learn from highly nonlinear correlations and (ii) ability to automatically select appropriate features at different network layers due to its built-in feature selection mechanism
Summary
Received 18 February 2021; Revised 4 April 2021; Accepted April 2021; Published July 2021. E 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. The framework for deep learning-based monthly prediction of the volumes of Macau tourist arrivals was presented. E main objective in this study is to predict the tourism growth via one of the deep learning algorithms of extracting new features. E 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
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