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How to capture tourists' search behavior in tourism forecasts? A two-stage feature selection approach 期刊论文
EXPERT SYSTEMS WITH APPLICATIONS, 2023, 卷号: 213, 页码: 9
Authors:  Sun, Shaolong;  Hu, Mengyuan;  Wang, Shouyang;  Zhang, Chengyuan
Favorite  |  View/Download：21/0  |  Submit date：2023/02/07
Tourism demand forecasting  Search engine data  Two -stage feature selection  Genetic algorithm  Kernel extreme learning machine  
IMPROVING MULTI-STEP AHEAD TOURISM DEMAND FORECASTING: A STRATEGY-DRIVEN APPROACH 期刊论文
EXPERT SYSTEMS WITH APPLICATIONS, 2022, 卷号: 210, 页码: 34
Authors:  Sun, Shaolong;  Du, Zongjuan;  Zhang, Chengyuan;  Wang, Shouyang
Favorite  |  View/Download：6/0  |  Submit date：2023/02/07
Tourism demand forecasting  Multi-step ahead strategies  MIMO  DIRMO  Extreme learning machine  
Multi-step ahead tourism demand forecasting: The perspective of the learning using privileged information paradigm 期刊论文
EXPERT SYSTEMS WITH APPLICATIONS, 2022, 卷号: 210, 页码: 12
Authors:  Sun, Shaolong;  Li, Mingchen;  Wang, Shouyang;  Zhang, Chengyuan
Favorite  |  View/Download：10/0  |  Submit date：2023/02/07
Tourism demand  Multi -step ahead forecasting  Privileged information  Machine learning  Kernel random vector functional link network  
Multi-scale analysis-driven tourism forecasting: insights from the peri-COVID-19 期刊论文
CURRENT ISSUES IN TOURISM, 2022, 页码: 24
Authors:  Li, Mingchen;  Zhang, Chengyuan;  Wang, Shouyang;  Sun, Shaolong
Favorite  |  View/Download：8/0  |  Submit date：2023/02/07
Tourism demand forecasting  divide and conquer  Facebook prophet  deep learning  peri-COVID-19 era  
A novel deep learning approach for tourism volume forecasting with tourist search data 期刊论文
INTERNATIONAL JOURNAL OF TOURISM RESEARCH, 2022, 页码: 15
Authors:  Li, Mingchen;  Zhang, Chengyuan;  Sun, Shaolong;  Wang, Shouyang
Favorite  |  View/Download：7/0  |  Submit date：2023/02/07
bi-directional gated recurrent unit  search engine data  stacked autoencoders  tourism volume forecasting  
Air quality forecasting with artificial intelligence techniques: A scientometric and content analysis 期刊论文
ENVIRONMENTAL MODELLING & SOFTWARE, 2022, 卷号: 149, 页码: 17
Authors:  Li, Yanzhao;  Guo, Ju-e;  Sun, Shaolong;  Li, Jianing;  Wang, Shouyang;  Zhang, Chengyuan
Favorite  |  View/Download：48/0  |  Submit date：2022/06/21
Air quality forecasting  Artificial intelligence  Machine learning  Scientometrics  Content analysis  
Air pollution forecasting with multivariate interval decomposition ensemble approach 期刊论文
ATMOSPHERIC POLLUTION RESEARCH, 2021, 卷号: 12, 期号: 12, 页码: 14
Authors:  Dong, Yawei;  Zhang, Chengyuan;  Niu, Mingfei;  Wang, Shouyang;  Sun, Shaolong
Favorite  |  View/Download：53/0  |  Submit date：2022/04/02
Daily PM 10 concentration forecast  Air quality  Interval forecasting  Noise-assisted multivariate empirical mode  decomposition  Maximum mutual information  
Forecasting hourly PM2.5 based on deep temporal convolutional neural network and decomposition method 期刊论文
APPLIED SOFT COMPUTING, 2021, 卷号: 113, 页码: 15
Authors:  Jiang, Fuxin;  Zhang, Chengyuan;  Sun, Shaolong;  Sun, Jingyun
Favorite  |  View/Download：56/0  |  Submit date：2022/04/29
PM2.5 concentration forecasting  Complete ensemble empirical mode  decomposition with adaptive noise  Temporal convolutional  Data patterns  Deep learning  
The impact of COVID-19 on hotel customer satisfaction: evidence from Beijing and Shanghai in China 期刊论文
INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT, 2021, 页码: 25
Authors:  Sun, Shaolong;  Jiang, Fuxin;  Feng, Gengzhong;  Wang, Shouyang;  Zhang, Chengyuan
Favorite  |  View/Download：67/0  |  Submit date：2022/04/02
COVID-19  Hotel reviews  Text mining  Word2vec  Cluster analysis  Topic model  
Decomposition Methods for Tourism Demand Forecasting: A Comparative Study 期刊论文
JOURNAL OF TRAVEL RESEARCH, 2021, 页码: 18
Authors:  Zhang, Chengyuan;  Li, Mingchen;  Sun, Shaolong;  Tang, Ling;  Wang, Shouyang
Favorite  |  View/Download：58/0  |  Submit date：2022/04/02
tourism demand forecasting  decomposition methods  variational mode decomposition  decomposition and ensemble  machine learning