研究成果：Enhancing tourism demand forecasting with a transformerbased framework

作者：李新，Yechi Xu，Rob Law，Shouyang Wang

发表期刊：Annals of Tourism Research

发表时间：2024年7月

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 finetuned 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.