Forecasting tourism demand with pre-holiday attribute

Yong Liu (), Xiang-jie Fu () and Jeffrey Lin Yi Forrest ()
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Yong Liu: Jiangnan University
Xiang-jie Fu: Jiangnan University
Jeffrey Lin Yi Forrest: Slippery Rock University of Pennsylvania Slippery Rock

Information Technology & Tourism, 2025, vol. 27, issue 3, No 5, 613-648

Abstract: Abstract Tourism demand forecasting is critical for decision-making in emergency resource allocation, and personnel management at tourist destinations. However, accurate predictions require the integration of multiple factors, such as network data of scenic areas, weather information, date-related data, and historical trends. To build a precise, robust, and generalizable forecasting model, this study introduces a novel “pre-holiday” feature for date information and employs the Transformer deep learning architecture as the core framework. The model integrates convolutional padding and serialization techniques to extract interval-based data features. Ultimately, it is designed to be applicable across diverse tourist destinations and varying forecasting time horizons, delivering end-to-end intelligent predictions—from data input to final output. Extensive ablation and comparative experiments show that the model is not only adaptable to single time-span forecasting but also achieves high-accuracy predictions for 15-day medium range forecasts. Compared with the existing mainstream research, the prediction error of the model is only 67% of the existing optimal model. Furthermore, the model demonstrates strong transferability across datasets from different scenic areas.

Keywords: Tourism demand; Time series prediction; Deep learning; Pre-holiday attribute (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s40558-025-00315-5

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