Adaptive tourism forecasting using hybrid artificial intelligence model: a case study of Xi’an international tourist arrivals
Accurate forecasting of tourist demand is important to both business practitioners and government policymakers. In the past decade of rapid development of deep learning, many artificial intelligence methods or deep learning models have been built to improve prediction accuracy. But data-driven end-t...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2023-11-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1573.pdf |
| _version_ | 1797427679273680896 |
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| author | Shuxin Zheng Zhongguo Zhang |
| author_facet | Shuxin Zheng Zhongguo Zhang |
| author_sort | Shuxin Zheng |
| collection | DOAJ |
| description | Accurate forecasting of tourist demand is important to both business practitioners and government policymakers. In the past decade of rapid development of deep learning, many artificial intelligence methods or deep learning models have been built to improve prediction accuracy. But data-driven end-to-end deep network models usually require large data sets to support. For tourism forecasting, the sample is insufficient and many models are difficult to apply. In this article, we propose a novel hybrid model GM-LSTM, which combines the advantages of gray models and neural networks to achieve self-adaptive prediction with small samples. Specifically, the overall trend of tourism demand is captured by a first-order gray model and the non-linear residual fluctuation is characterized using a long short-term memory (LSTM) network with a rolling mechanism. The model is validated through a case study of up to 38 years of data on annual international tourist arrivals in Xi’an, China. The proposed GM-LSTM model achieved a predicted MAPE value of 11.88%, outperforming other time series models. The results indicate that our proposed hybrid model is accurate and efficient. |
| first_indexed | 2024-03-09T08:48:31Z |
| format | Article |
| id | doaj.art-f6058f39a86f442098975d6adbae7279 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-03-09T08:48:31Z |
| publishDate | 2023-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-f6058f39a86f442098975d6adbae72792023-12-02T15:05:05ZengPeerJ Inc.PeerJ Computer Science2376-59922023-11-019e157310.7717/peerj-cs.1573Adaptive tourism forecasting using hybrid artificial intelligence model: a case study of Xi’an international tourist arrivalsShuxin Zheng0Zhongguo Zhang1School of Economics and Business, Changzhou Vocational Institute of Textile and Garment, Changzhou, ChinaCollege of Computer Science and Technology, Taizhou University, Taizhou, ChinaAccurate forecasting of tourist demand is important to both business practitioners and government policymakers. In the past decade of rapid development of deep learning, many artificial intelligence methods or deep learning models have been built to improve prediction accuracy. But data-driven end-to-end deep network models usually require large data sets to support. For tourism forecasting, the sample is insufficient and many models are difficult to apply. In this article, we propose a novel hybrid model GM-LSTM, which combines the advantages of gray models and neural networks to achieve self-adaptive prediction with small samples. Specifically, the overall trend of tourism demand is captured by a first-order gray model and the non-linear residual fluctuation is characterized using a long short-term memory (LSTM) network with a rolling mechanism. The model is validated through a case study of up to 38 years of data on annual international tourist arrivals in Xi’an, China. The proposed GM-LSTM model achieved a predicted MAPE value of 11.88%, outperforming other time series models. The results indicate that our proposed hybrid model is accurate and efficient.https://peerj.com/articles/cs-1573.pdfTourist demandHybrid gray modelArtificial neural networkSmall-sample learning |
| spellingShingle | Shuxin Zheng Zhongguo Zhang Adaptive tourism forecasting using hybrid artificial intelligence model: a case study of Xi’an international tourist arrivals PeerJ Computer Science Tourist demand Hybrid gray model Artificial neural network Small-sample learning |
| title | Adaptive tourism forecasting using hybrid artificial intelligence model: a case study of Xi’an international tourist arrivals |
| title_full | Adaptive tourism forecasting using hybrid artificial intelligence model: a case study of Xi’an international tourist arrivals |
| title_fullStr | Adaptive tourism forecasting using hybrid artificial intelligence model: a case study of Xi’an international tourist arrivals |
| title_full_unstemmed | Adaptive tourism forecasting using hybrid artificial intelligence model: a case study of Xi’an international tourist arrivals |
| title_short | Adaptive tourism forecasting using hybrid artificial intelligence model: a case study of Xi’an international tourist arrivals |
| title_sort | adaptive tourism forecasting using hybrid artificial intelligence model a case study of xi an international tourist arrivals |
| topic | Tourist demand Hybrid gray model Artificial neural network Small-sample learning |
| url | https://peerj.com/articles/cs-1573.pdf |
| work_keys_str_mv | AT shuxinzheng adaptivetourismforecastingusinghybridartificialintelligencemodelacasestudyofxianinternationaltouristarrivals AT zhongguozhang adaptivetourismforecastingusinghybridartificialintelligencemodelacasestudyofxianinternationaltouristarrivals |