Interpretable Tourism Demand Forecasting with Two-Stage Decomposition and Temporal Fusion Transformers

This paper proposes a novel interpretable tourism demand forecasting framework that considers the impact of the COVID-19 pandemic by using multi-source heterogeneous data, namely, historical tourism volume, newly confirmed cases in tourist origins and destinations, and search engine data. This paper introduces newly confirmed cases in tourist origins and tourist destinations to forecast tourism demand and proposes a new two-stage decomposition method called ensemble empirical mode decomposition-variational mode decomposition to deal with the tourist arrival sequence. To solve the problem of insufficient interpretability of existing tourism demand forecasting, this paper also proposes a novel interpretable tourism demand forecasting model called JADE-TFT, which utilizes an adaptive differential evolution algorithm with external archiving (JADE) to intelligently and efficiently optimize the hyperparameters of temporal fusion transformers (TFT). The validity of the proposed prediction framework is verified by actual cases based on Hainan and Macau tourism data sets. The interpretable experimental results show that newly confirmed cases in tourist origins and tourist destinations can better reflect tourists' concerns about travel in the post-pandemic era, and the two-stage decomposition method can effectively identify the inflection point of tourism prediction, thereby increasing the prediction accuracy of tourism demand.