Forecasting International Tourism Demand in China Based on Comparison of Three Models
Abstract
Tourism is an important part of the national economy, accuracy forecasting tourism demand is conducive to promoting the sustainable development of the tourism industry. In order to forecast international tourism demand in China, this paper uses the monthly tourist arrivals from China to Thailand, Japan and Korea time span from January 2011 to December 2019, and consider Baidu search engine as exogenous variable. Using three commonly used forecasting models, namely, seasonal autoregressive integrated moving average with exogenous variable (SARMAX) model, back propagation neural network (BPNN) model and support vector regression (SVR) model to long term and short term forecast the international tourism demand in China. The results show that the SARIMAX model generate highest prediction accuracy for almost all evaluation indicators and forecasting steps, while BPNN model and SVR model show different forecasting accuracy under different conditions, which provides guidance for the selection of forecasting models for tourism demand.
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DOI: http://dx.doi.org/10.18686/mmf.v6i9.9977
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