Cited By
View all- Dong YZhou BYang GHou FHu ZMa S(2023)A novel model for tourism demand forecasting with spatial–temporal feature enhancement and image-driven methodNeurocomputing10.1016/j.neucom.2023.126663556:COnline publication date: 1-Nov-2023
A Combination Model Based Deep Long Term Model for Tourism Demand Forecasting
Authors: 
Yunxuan Dong, Ling XiaoAuthors Info & Claims
Yunxuan Dong, Ling XiaoAuthors Info & ClaimsPages 126 - 131
Abstract
The accurate tourism demand forecasting is crucial to the development of tourism. However, the challenge of non-linear features recognizing in tourism time series makes it a troublesome thing. To overcome the above difficulties, this paper proposes a novel model for tourism demand forecasting based on a long term recurrent neural network with an evolutionary optimization algorithm. The model aims to learn the features of tourism demand time series by combining several sequences, and the proposed model consists of two sections, the first section defines the employed neural network; the second section introduces the optimization algorithm to search the optimal weights for difference sequences. Tourism demand time series of Macau has been adopted to validate the proposed model, and the experimental results show that the proposed method can accurately forecast the daily tourism demand of Macau, China.
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- A Combination Model Based Deep Long Term Model for Tourism Demand Forecasting
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Published In
February 2022
202 pages
ISBN:9781450387453
DOI:10.1145/3523181
Copyright © 2022 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 18 April 2022
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- Research-article
- Research
- Refereed limited
Funding Sources
- the Science and Technology Innovation Project of The Chengdu-Chongqing Twin Cities Economic Zone
- the Science and Technology Research Program of CHongqing Municipal Municipal Education Commission
- Education Reform Project of Chongqing University of Posts and Telecommunications
Conference
ASSE' 22
ASSE' 22: 2022 3rd Asia Service Sciences and Software Engineering Conference
February 24 - 26, 2022
Macau, Macao
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View all- Dong YZhou BYang GHou FHu ZMa S(2023)A novel model for tourism demand forecasting with spatial–temporal feature enhancement and image-driven methodNeurocomputing10.1016/j.neucom.2023.126663556:COnline publication date: 1-Nov-2023
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