What is it about?

The paper is about improving tourism demand forecasting with AI-based methods.

Featured Image

Why is it important?

Tourism planners, destinations, hotels, airlines, retailers, event managers all need to know how many tourists plan a trip to a particular destination. The more accurate this forecasts are the better for strategic and operational planning for businesses and governments.

Perspectives

Tourism demand forecasting is immensely important for tourism and other sectors, such as economic and resource management studies. However, achieving model accuracy is complicated. Even with innovative AI-based methodologies challenges remain. One manor challenge is model overfitting. We use AI-based approach to better manage model overfitting and develop a group-pooling-based deep-learning model (GP–DLM). Addressing the overfitting problem helps to improve accuracy in tourism demand forecasting.

Dr Birgit Muskat
Australian National University

Read the Original

This page is a summary of: Group pooling for deep tourism demand forecasting, Annals of Tourism Research, May 2020, Elsevier, DOI: 10.1016/j.annals.2020.102899.
You can read the full text:

Read

Resources

Contributors

The following have contributed to this page