Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1784894201723355136 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9954797 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT chenjingyao forecastingtouristarrivalsforhainanislandinchinawithdecomposedbroadlearningbeforethecovid19pandemic AT yangjie forecastingtouristarrivalsforhainanislandinchinawithdecomposedbroadlearningbeforethecovid19pandemic AT huangshigao forecastingtouristarrivalsforhainanislandinchinawithdecomposedbroadlearningbeforethecovid19pandemic AT lixin forecastingtouristarrivalsforhainanislandinchinawithdecomposedbroadlearningbeforethecovid19pandemic AT liugang forecastingtouristarrivalsforhainanislandinchinawithdecomposedbroadlearningbeforethecovid19pandemic |