Forecasting Tourist Arrivals for Hainan Island in China with Decomposed Broad Learning before the COVID-19 Pandemic

Jingyao Chen; Jie Yang; Shigao Huang; Xin Li; Gang Liu

doi:10.3390/e25020338

Forecasting Tourist Arrivals for Hainan Island in China with Decomposed Broad Learning before the COVID-19 Pandemic

Entropy (Basel). 2023 Feb 12;25(2):338. doi: 10.3390/e25020338.

Authors

Jingyao Chen¹, Jie Yang², Shigao Huang³, Xin Li¹, Gang Liu⁴

Affiliations

¹ School of Business, Macau University of Science and Technology, Macau SAR, China.
² College of Artificial Intelligence, Chongqing Industry & Trade Polytechnic, Chongqing 408000, China.
³ Faculty of Health Science, University of Macau, Macau SAR, China.
⁴ Tourism School, Hainan University, 58 Renmin Road, Haikou 570228, China.

Abstract

This study proposes a decomposed broad learning model to improve the forecasting accuracy for tourism arrivals on Hainan Island in China. With decomposed broad learning, we predicted monthly tourist arrivals from 12 countries to Hainan Island. We compared the actual tourist arrivals to Hainan from the US with the predicted tourist arrivals using three models (FEWT-BL: fuzzy entropy empirical wavelet transform-based broad learning; BL: broad Learning; BPNN: back propagation neural network). The results indicated that US foreigners had the most arrivals in 12 countries, and FEWT-BL had the best performance in forecasting tourism arrivals. In conclusion, we establish a unique model for accurate tourism forecasting that can facilitate decision-making in tourism management, especially at turning points in time.

Keywords: broad learning; empirical wavelet transform; fuzzy entropy; tourism arrivals; tourism forecasting.

Grants and funding

National Social Science Foundation Project (21XSH019), Hainan Philosophy and Social Science Planning Project: Hainan Free Trade Port-Guangdong-Hong Kong-Macao Greater Bay Area Strategic Coordination and Coordinated Development Research (HNSK (ZC) 20-04). MOE (Ministry of Education in China), Project of Humanities and Social Science (Project No.22YJCZH213), The Natural Science Foundation of Chongqing, China (No. cstc2021jcyj-msxmX1108), 2021 General Project of Humanities and Social Sciences Research of Chongqing Municipal Education Commission (21SKGH362), the project of Chongqing Industry and Trade Polytechnic (No. ZR202111), and the project of Science and Technology Research Program of Chongqing Municipal Education Commission of China (Grant KJZD-K202203601, No. KJQN202203605, No. KJQN202203607).