Academic Journal of Computing & Information Science, 2023, 6(9); doi: 10.25236/AJCIS.2023.060911.
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
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.
 Song, H., & Li, G. (2008). Tourism demand modelling and forecasting—A review of recent research. Tourism management, 29(2), 203-220.
 Kulendran, N., & Wong, K. K. (2005). Modeling seasonality in tourism forecasting. Journal of Travel Research, 44(2), 163-170.
 Guizzardi, A., & Mazzocchi, M. (2010). Tourism demand for Italy and the business cycle. Tourism Management, 31(3), 367-377.
 Gunter, U., & Önder, I. (2016). Forecasting city arrivals with Google Analytics. Annals of Tourism Research, 61, 199-212.
 Lendasse, A., Oja, E., Simula, O., & Verleysen, M. (2007). Time series prediction competition: The CATS benchmark. Neurocomputing, 70(13-15), 2325-2329.
 Moore, W. R. (2010). The impact of climate change on Caribbean tourism demand. Current Issues in Tourism, 13(5), 495-505.
 Wan, S. K., & Song, H. (2018). Forecasting turning points in tourism growth. Annals of Tourism Research, 72, 156-167.
 Smeral, E., & Song, H. (2015). Varying elasticities and forecasting performance. International Journal of Tourism Research, 17(2), 140-150.
 Hu, M., & Song, H. (2020). Data source combination for tourism demand forecasting. Tourism Economics, 26(7), 1248-1265.
 Alshanbari, H. M., Mehmood, T., Sami, W., Alturaiki, W., Hamza, M. A., & Alosaimi, B. (2022). Prediction and Classification of COVID-19 Admissions to Intensive Care Units (ICU) Using Weighted Radial Kernel SVM Coupled with Recursive Feature Elimination (RFE). Life, 12(7), 1100.
 Adil, M., Wu, J. Z., Chakrabortty, R. K., Alahmadi, A., Ansari, M. F., & Ryan, M. J. (2021). Attention-based STL-BiLSTM network to forecast tourist arrival. Processes, 9(10), 1759.
 Zhang, C., & Tian, Y. X. (2022). Forecast daily tourist volumes during the epidemic period using COVID-19 data, search engine data and weather data. Expert Systems with Applications, 210, 118505.
 Chen, Y. C., & Huang, W. C. (2021). Constructing a stock-price forecast CNN model with gold and crude oil indicators. Applied Soft Computing, 112, 107760.
 Law, R., Li, G., Fong, D. K. C., & Han, X. (2019). Tourism demand forecasting: A deep learning approach. Annals of tourism research, 75, 410-423.
 Yang, X., Pan, B., Evans, J. A., & Lv, B. (2015). Forecasting Chinese tourist volume with search engine data. Tourism management, 46, 386-397.
 Li, C., Ge, P., Liu, Z., & Zheng, W. (2020). Forecasting tourist arrivals using denoising and potential factors. Annals of Tourism Research, 83, 102943.
 Zhang, C., Tian, Y. X., & Fan, Z. P. (2022). Forecasting sales using online review and search engine data: A method based on PCA–DSFOA–BPNN. International Journal of Forecasting, 38(3), 1005-1024.
 Li, S., Chen, T., Wang, L., & Ming, C. (2018). Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index. Tourism Management, 68, 116-126.
 Li, X., Li, H., Pan, B., & Law, R. (2021). Machine learning in internet search query selection for tourism forecasting. Journal of Travel Research, 60(6), 1213-1231.
 Darst, B. F., Malecki, K. C., & Engelman, C. D. (2018). Using recursive feature elimination in random forest to account for correlated variables in high dimensional data. BMC genetics, 19(1), 1-6.
 Xie, G., Li, X., Qian, Y., & Wang, S. (2021). Forecasting tourism demand with KPCA-based web search indexes. Tourism Economics, 27(4), 721-743.
 Li, X., Pan, B., Law, R., & Huang, X. (2017). Forecasting tourism demand with composite search index. Tourism management, 59, 57-66.
 Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: a review and recent developments. Philosophical transactions of the royal society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202.
 Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.