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

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.

Automated summary

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. Comparing the actual tourist arrivals from the US with the predicted tourist arrivals using three models (FEWT-BL, BL, BPNN), it was found that the US had the highest number of arrivals among the 12 countries, and FEWT-BL performed the best in forecasting tourism arrivals. In conclusion, this unique model can facilitate decision-making in tourism management, especially at turning points in time.