What is it about?

Tourism planners rely on accurate demand forecasting. However, despite numerous advancements, crucial methodological issues remain unaddressed. This study improves the modeling accuracy. We use artificial intelligence (AI)-based tourism demand forecasting methods. Deep learning models that predict tourism demand are often highly complex and encounter overfitting, which is mainly caused by two underlying problems: (1) access to limited data volumes and (2) additional explanatory variable requirement.

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Why is it important?

This paper is important because it explains how to improve modeling accuracy in AI-based forecasting approaches.

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This page is a summary of: Tourism Demand Forecasting: A Decomposed Deep Learning Approach, Journal of Travel Research, June 2020, SAGE Publications, DOI: 10.1177/0047287520919522.
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