Tourism Demand Forecasting: A Decomposed Deep Learning Approach

Abstract

Tourism planners rely on accurate demand forecasting. However, despite numerous advancements, crucial methodological issues remain unaddressed. This study aims to further improve the modeling accuracy and advance the 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. To address these issues, we use a decomposition method that achieves high accuracy in short- and long-term AI-based forecasting models. The proposed method effectively decomposes the data and increases accuracy without additional data requirement. In conclusion, this study alleviates the overfitting issue and provides a methodological contribution by proposing a highly accurate deep learning method for AI-based tourism demand modeling.

20 years of Nordic tourism economics research: a review and future research agenda

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“…Several prediction models are available in the field of TD prediction. In [8], the authors suggested that these methods are classified into time-series, AI, and econometric models, but TD prediction is usually done using time-series and econometric models [33].…”

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“…Uma boa previsão da demanda proporcionará menor estoque, custo financeiro e tempo de entrega, maior previsibilidade e satisfação do cliente (ZHANG et al, 2020). A sazonalidade é uma característica frequente na demanda, causada por variações climáticas, datas comemorativas, entre outros fatores.…”