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

作　　者：WU Binrong WANG Lin ZENG Yu-Rong 

机构地区：[1]Business School,Hohai University,Nanjing 211100,China [2]School of Management,Huazhong University of Science and Technology,Wuhan 430074,China [3]School of Information Engineering,Hubei University of Economics,Wuhan 430205,China

出　　处：《Journal of Systems Science & Complexity》2024年第6期2654-2679,共26页系统科学与复杂性学报（英文版）

基　　金：partially supported by the Humanities and Social Sciences Foundation of the Chinese Ministry of Education of China under Grant No.22YJA630003。

摘　　要：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 Macao 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.

关 键 词：COVID-19 interpretable deep learning search engine data tourism demand forecasting 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] F592.7[自动化与计算机技术—控制科学与工程]