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

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.

Forecasting occurrence of palm weevil Rhynchophorus palmarum L. (Coleoptera, Curculionidae) using Autoregressive Integrated Moving Average modeling

“…It is important to investigate the use of other types of models to forecast the BWTSD and compare their performance. Recent studies have shown that hybrid models combining ARIMA with artificial intelligence (AI) algorithms have produced superior results in predicting insect populations (Chen et al, 2023;Zhao et al, 2023). In addition, it would be worthwhile to compare the performance of ARIMA models with other time series forecasting techniques such as exponential smoothing or seasonal decomposition (Svetunkov et al, 2023).…”