Support vector regression with genetic algorithms in forecasting tourism demand

Abstract

This study applies a novel neural network technique, support vector regression (SVR), to tourism demand forecasting. The aim of this study is to examine the feasibility of SVR in tourism demand forecasting by comparing it with back-propagation neural networks (BPNN) and the autoregressive integrated moving average (ARIMA) model. To build an effective SVR model, SVR's parameters must be set carefully. This study proposes a novel approach, known as genetic algorithm (GA)-SVR, which searches for SVR's optimal parameters using real value GAs, and then adopts the optimal parameters to construct the SVR models. The tourist arrivals to China during 1985–2001 were employed as the data set. The experimental results demonstrate that SVR outperforms the BPNN and ARIMA models based on the normalized mean square error (NMSE) and mean absolute percentage error (MAPE).