Enhancing Tourism Demand Forecasting with a Transformer-based Framework

Xin Li, Yechi Xu, Rob Law and Shouyang Wang

No 5ezn3_v1, SocArXiv from Center for Open Science

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.

Date: 2024-04-18
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:5ezn3_v1

DOI: 10.31219/osf.io/5ezn3_v1

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