Tourism demand forecasting: A deep learning approach

By R. Law, Gang Li, D. Fong, Xin Han. Annals of Tourism Research, 2019

413 citations recorded by the source corpus.

Traditional tourism demand forecasting models may face challenges when massive amounts of search intensity indices are adopted as tourism demand indicators. Using a deep learning approach, this research studied the framework in forecasting monthly Macau tourist arrival volumes. The empirical results demonstrated that the deep learning approach significantly outperforms support vector regression and artificial neural network models. Moreover, the construction and identification of highly relevant features from the proposed deep network architecture provide practitioners with a means of understanding the relationships between various tourist demand forecasting factors and tourist arrival volumes.

Subfields: Sociology and Political Science, Tourism, Leisure and Hospitality Management

View Paper: https://www.semanticscholar.org/paper/22674e43aeaa781ff397c050a6cef90a77ed19fc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Sociology and Political Science

1. Pritee Ray
2. Ratri Parida
3. Sanjita Jaipuria

In Tourism, Leisure and Hospitality Management

1. Pritee Ray
2. Ratri Parida
3. Sanjita Jaipuria

Citations in the corpus

• The impact of COVID-19 on tourism sector in India (2020, background): “Machine and deep learning methods are more adaptable and can generate high accurate result (Law et al., 2019; Polyzos et al., 2020).”