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.

中文翻译：

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基于深度学习方法的旅游增长预测

由于用作旅游需求指标的搜索强度指数过多，传统的旅游需求预测模型目前面临着一些挑战。在这项工作中，提出了基于深度学习的澳门旅游人数月度预测框架。本研究的主要目标是通过一种提取新特征的深度学习算法来预测旅游增长。这项研究的结果表明，所采用的深度学习框架的性能优于人工神经网络和支持向量回归模型。从业者可以依靠从开发的框架中识别出的相关特征来了解旅游需求预测因素与实际到访人数之间关系的性质。