Tourism Growth Prediction Based on Deep Learning Approach
The conventional tourism demand prediction models are currently facing several challenges due to the excess number of search intensity indices that are used as indicators of tourism demand. In this work, the framework for deep learning-based monthly prediction of the volumes of Macau tourist arrival...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5531754 |
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| _version_ | 1832548792279236608 |
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| author | Xiaoling Ren Yanyan Li JuanJuan Zhao Yan Qiang |
| author_facet | Xiaoling Ren Yanyan Li JuanJuan Zhao Yan Qiang |
| author_sort | Xiaoling Ren |
| collection | DOAJ |
| description | The conventional tourism demand prediction models are currently facing several challenges due to the excess number of search intensity indices that are used as indicators of tourism demand. In this work, the framework for deep learning-based monthly prediction of the volumes of Macau tourist arrivals was presented. The main objective in this study is to predict the tourism growth via one of the deep learning algorithms of extracting new features. The outcome of this study showed that the performance of the adopted deep learning framework was better than that of artificial neural network and support vector regression models. Practitioners can rely on the identified relevant features from the developed framework to understand the nature of the relationships between the predictive factors of tourist demand and the actual volume of tourist arrival. |
| format | Article |
| id | doaj-art-0439ce2c8c3e43dab414598d06cbda50 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-0439ce2c8c3e43dab414598d06cbda502025-02-03T06:12:56ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55317545531754Tourism Growth Prediction Based on Deep Learning ApproachXiaoling Ren0Yanyan Li1JuanJuan Zhao2Yan Qiang3College of Economics and Management, Taiyuan University of Technology, Jinzhong 030600, ChinaCollege of History and Culture, Shanxi University, Taiyuan 030006, ChinaCollege of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, ChinaCollege of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, ChinaThe conventional tourism demand prediction models are currently facing several challenges due to the excess number of search intensity indices that are used as indicators of tourism demand. In this work, the framework for deep learning-based monthly prediction of the volumes of Macau tourist arrivals was presented. The main objective in this study is to predict the tourism growth via one of the deep learning algorithms of extracting new features. The outcome of this study showed that the performance of the adopted deep learning framework was better than that of artificial neural network and support vector regression models. Practitioners can rely on the identified relevant features from the developed framework to understand the nature of the relationships between the predictive factors of tourist demand and the actual volume of tourist arrival.http://dx.doi.org/10.1155/2021/5531754 |
| spellingShingle | Xiaoling Ren Yanyan Li JuanJuan Zhao Yan Qiang Tourism Growth Prediction Based on Deep Learning Approach Complexity |
| title | Tourism Growth Prediction Based on Deep Learning Approach |
| title_full | Tourism Growth Prediction Based on Deep Learning Approach |
| title_fullStr | Tourism Growth Prediction Based on Deep Learning Approach |
| title_full_unstemmed | Tourism Growth Prediction Based on Deep Learning Approach |
| title_short | Tourism Growth Prediction Based on Deep Learning Approach |
| title_sort | tourism growth prediction based on deep learning approach |
| url | http://dx.doi.org/10.1155/2021/5531754 |
| work_keys_str_mv | AT xiaolingren tourismgrowthpredictionbasedondeeplearningapproach AT yanyanli tourismgrowthpredictionbasedondeeplearningapproach AT juanjuanzhao tourismgrowthpredictionbasedondeeplearningapproach AT yanqiang tourismgrowthpredictionbasedondeeplearningapproach |