Enhancing tourism demand forecasting with a transformer-based framework

Xin Li, Yechi Xu, Rob Law and Shouyang Wang

Annals of Tourism Research, 2024, vol. 107, issue C

Abstract: This study introduces an innovative framework that harnesses the most recent transformer architecture to enhance tourism demand forecasting. The proposed transformer-based model integrates the tree-structured parzen estimator for hyperparameter optimization, a robust time series decomposition approach, and a temporal fusion transformer for multivariate time series prediction. Our novel approach initially employs the decomposition method to decompose the data series to effectively mitigate the influence of outliers. The temporal fusion transformer is subsequently utilized for forecasting, and its hyperparameters are meticulously fine-tuned by a Bayesian-based algorithm, culminating in a more efficient and precise model for tourism demand forecasting. Our model surpasses existing state-of-the-art methodologies in terms of forecasting accuracy and robustness.

Keywords: Tourism demand forecasting; Time series decomposition; Temporal fusion transformer; Tree-structured parzen estimator (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:anture:v:107:y:2024:i:c:s0160738324000689

DOI: 10.1016/j.annals.2024.103791

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