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Academic Journal of Computing & Information Science, 2023, 6(9); doi: 10.25236/AJCIS.2023.060911.

Tourism demand forecasting using PCA-BPNN


Xin Zhao

Corresponding Author:
Xin Zhao

College of Economics, China-ASEAN Institute of Financial Cooperation, Guangxi University, Nanning, 530004, China


Accurate prediction of tourism demand is critically important for the efficient allocation of resources in scenic areas and managing sudden events. This paper presents a new tourism demand prediction model, PCA-BPNN neural network model. It utilizes Principal Component Analysis (PCA) to reduce the dimensionality of the collected Baidu Index data and mitigate overfitting issues. The model then constructs a backpropagation neural network (BPNN). Empirical research demonstrates that PCA-BPNN effectively identifies the nonlinear relationship between search keywords and the number of tourist arrivals and outperforms all benchmark models in terms of predictive performance.


Tourism demand forecasting; Search engine data; PCA-BPNN; PCA

Cite This Paper

Xin Zhao. Tourism demand forecasting using PCA-BPNN. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 9: 72-80. https://doi.org/10.25236/AJCIS.2023.060911.


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