The Research on the forecast of tourism demand based on Baidu search index--Taking Beijing as an example

Abstract

The accurate analysis of tourist volume plays an important role in the scientific management of tourism resources and the formulation of policies by tourism decision makers. The data generated by tourists' online search behavior provide a new perspective for tourism prediction. Based on the six elements of tourism, combined with text analysis, the conceptual framework of Baidu index keywords related to tourist volume is established. The statistical test and correlation analysis of Baidu index keywords were carried out, and the keyword sequences with predictive ability were selected. Then several prediction models are established to predict the number of visitors and optimize the model. Finally, the prediction accuracy of different models is analyzed by using goodness of fit (R2) and mean absolute percentage error (MAPE). It is found that the variable weight combination prediction model based on GBDT has the best effect, $\mathrm{R}^{2}$ and MAPE are 0.9943 and 1.78%, respectively.