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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 t...

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Detalles Bibliográficos
Autores principales: Chen, Jingyao, Yang, Jie, Huang, Shigao, Li, Xin, Liu, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9954797/
https://www.ncbi.nlm.nih.gov/pubmed/36832704
http://dx.doi.org/10.3390/e25020338
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author Chen, Jingyao
Yang, Jie
Huang, Shigao
Li, Xin
Liu, Gang
author_facet Chen, Jingyao
Yang, Jie
Huang, Shigao
Li, Xin
Liu, Gang
author_sort Chen, Jingyao
collection PubMed
description 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.
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spelling pubmed-99547972023-02-25 Forecasting Tourist Arrivals for Hainan Island in China with Decomposed Broad Learning before the COVID-19 Pandemic Chen, Jingyao Yang, Jie Huang, Shigao Li, Xin Liu, Gang Entropy (Basel) Article 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. MDPI 2023-02-12 /pmc/articles/PMC9954797/ /pubmed/36832704 http://dx.doi.org/10.3390/e25020338 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Jingyao
Yang, Jie
Huang, Shigao
Li, Xin
Liu, Gang
Forecasting Tourist Arrivals for Hainan Island in China with Decomposed Broad Learning before the COVID-19 Pandemic
title Forecasting Tourist Arrivals for Hainan Island in China with Decomposed Broad Learning before the COVID-19 Pandemic
title_full Forecasting Tourist Arrivals for Hainan Island in China with Decomposed Broad Learning before the COVID-19 Pandemic
title_fullStr Forecasting Tourist Arrivals for Hainan Island in China with Decomposed Broad Learning before the COVID-19 Pandemic
title_full_unstemmed Forecasting Tourist Arrivals for Hainan Island in China with Decomposed Broad Learning before the COVID-19 Pandemic
title_short Forecasting Tourist Arrivals for Hainan Island in China with Decomposed Broad Learning before the COVID-19 Pandemic
title_sort forecasting tourist arrivals for hainan island in china with decomposed broad learning before the covid-19 pandemic
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9954797/
https://www.ncbi.nlm.nih.gov/pubmed/36832704
http://dx.doi.org/10.3390/e25020338
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