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Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting

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 foreca...

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Autores principales: Liu, Hsiou-Hsiang, Chang, Lung-Cheng, Li, Chien-Wei, Yang, Cheng-Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6169209/
https://www.ncbi.nlm.nih.gov/pubmed/30327666
http://dx.doi.org/10.1155/2018/6076475
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author Liu, Hsiou-Hsiang
Chang, Lung-Cheng
Li, Chien-Wei
Yang, Cheng-Hong
author_facet Liu, Hsiou-Hsiang
Chang, Lung-Cheng
Li, Chien-Wei
Yang, Cheng-Hong
author_sort Liu, Hsiou-Hsiang
collection PubMed
description 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.
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spelling pubmed-61692092018-10-16 Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting Liu, Hsiou-Hsiang Chang, Lung-Cheng Li, Chien-Wei Yang, Cheng-Hong Comput Intell Neurosci Research Article 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. Hindawi 2018-09-19 /pmc/articles/PMC6169209/ /pubmed/30327666 http://dx.doi.org/10.1155/2018/6076475 Text en Copyright © 2018 Hsiou-Hsiang Liu et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Liu, Hsiou-Hsiang
Chang, Lung-Cheng
Li, Chien-Wei
Yang, Cheng-Hong
Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting
title Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting
title_full Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting
title_fullStr Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting
title_full_unstemmed Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting
title_short Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting
title_sort particle swarm optimization-based support vector regression for tourist arrivals forecasting
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6169209/
https://www.ncbi.nlm.nih.gov/pubmed/30327666
http://dx.doi.org/10.1155/2018/6076475
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