Research on Medium-term Prediction of Tourist Arrivals in Scenic Areas Based on Least Squares Support Vector Machines

Forecasting tourism demand plays an important role in formulating national tourism development policy and strategic planning.It is also important in optimizing the allocation of tourism market resources and drawing up strategic plans and decision making for tourism businesses.As a result,forecasting tourist arrivals has been a main focus in tourism research.However,making such forecasts is a difficult task for tourism researchers.This is particularly the case with widely fluctuating numbers of tourist arrivals in scenic areas,which are impacted by seasonality,external shocks and economic cycles.Making such medium-and long-term forecasts is one of the most challenging areas in tourism research. In this paper,the widely used method of least squares support vector machines was applied in forecasting two-year tourist arrivals in Huangshan scenic areas using monthly data(from January 1987 to December 2010). The analysis results showed that forecast performance indexes using least squares support vector machine algorithms are better than with the BP neural network,ARIMA forecasting model,and combined forecasting using empirical mode decomposition and least squares support vector machines.Moreover,least squares support vector machine algorithms are superior in both prediction accuracy and computing speed.The ability of least squares support vector machines to forecast tourist arrivals has high practical value and can provide a scientific basis for planning and strategy management of scenic areas. In addition,nine forecasting models were assessed: the time-series regression prediction model,the Holt-Winters addition and multiplication model,the TRAMO/SEATS model,a combination of the wavelet and ARIMA forecasting models,the wavelet neural network prediction method,and the four methods cited in the previous paragraph.We investigated the forecasting ability of these nine models in predicting tourist arrivals over one-and two-year time scales in Huangshan scenic areas.Owing to restrictions of space,however,we are unable to detail all the analysis procedures in this paper.The results showed that least squares support vector machines was superior in performance.It was better than the other single forecasting methods and also exceeded the combination of the wavelet and ARIMA forecasting models,the wavelet neural network,and the combined empirical mode decomposition and least squares support vector machines over the two-year time scale;however,these advantages were not evident over the one-year time scale.The effectiveness of least squares support vector machines in forecasting other tourist arrival data requires further examination. The main difficulties in forecasting monthly tourist arrival data lie in the large fluctuations in the numbers of tourists and the lack of data relating to tourist arrivals.These data include such economic figures as GDP,quarterly unit statistics,tourist arrival data other than annual statistics of inbound tourists,the vulnerability to external shocks,and diversity in the data from different regions.In all,the diversity,complexity,and fluctuation in different types of tourist arrivals make it difficult to find a satisfactory tourist arrival forecasting method that is universally accurate.Thus,greater efforts need to be made to develop better methods for accurately forecasting tourist arrival data for different scenic areas.