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

Hsiou-Hsiang Liu · Lung-Cheng Chang · Chien-Wei Li · Cheng‐Hong Yang

34 citations0/0 files storedComputational Intelligence and Neuroscience

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Forecasting Techniques and Applications Metaheuristic Optimization Algorithms Research Stock Market Forecasting Methods

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.

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2003Time series forecasting using a hybrid ARIMA and neural network model 1998Forecasting with artificial neural networks:2007Tourism demand modelling and forecasting—A review of recent research 2001Application of support vector machines in financial time series forecasting 2002A state space framework for automatic forecasting using exponential smoothing methods 2014Has the tourism-led growth hypothesis been validated? A literature review 2006Support vector regression with genetic algorithms in forecasting tourism demand 2009Tourism expansion, tourism uncertainty and economic growth: New evidence from Taiwan and Korea

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