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

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ID: 68743
2018
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Abstract
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.
Reference Key
liu2018particlecomputational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Liu, Hsiou-Hsiang;Chang, Lung-Cheng;Li, Chien-Wei;Yang, Cheng-Hong;
Journal Computational Intelligence and Neuroscience
Year 2018
DOI
10.1155/2018/6076475
URL
Keywords
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