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Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting
Joint Authors
Liu, Hsiou-Hsiang
Chang, Lung-Cheng
Li, Chien-Wei
Yang, Cheng-Hong
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-09-19
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
The tourism industry has become one of the most important economic sectors for governments worldwide.
Accurately forecasting tourism demand is crucial because it provides useful information to related industries and governments, enabling stakeholders to adjust plans and policies.
To develop a forecasting tool for the tourism industry, this study proposes a method that combines feature selection (FS) and support vector regression (SVR) with particle swarm optimization (PSO), named FS–PSOSVR.
To ensure high forecast accuracy, FS and a PSO algorithm are employed to, respectively, select reliable input variables and to identify the optimal initial parameters of SVR.
The proposed method was tested using a data set of monthly tourist arrivals to Taiwan from January 2006 to December 2016.
The results reveal that the errors obtained using FS–PSOSVR are comparatively smaller than those obtained using other methods, indicating that FS–PSOSVR is an effective method for forecasting tourism demand.
American Psychological Association (APA)
Liu, Hsiou-Hsiang& Chang, Lung-Cheng& Li, Chien-Wei& Yang, Cheng-Hong. 2018. Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1130789
Modern Language Association (MLA)
Liu, Hsiou-Hsiang…[et al.]. Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1130789
American Medical Association (AMA)
Liu, Hsiou-Hsiang& Chang, Lung-Cheng& Li, Chien-Wei& Yang, Cheng-Hong. Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1130789
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130789