Forecasting tourism demand with search engine data: A hybrid CNN-BiLSTM model based on Boruta feature selection

Abstract

Using search engine data (SED) to forecast tourist flow is essential for management and security warnings at tourist attractions. Existing prediction models cannot effectively handle noise in the SED and external uncertainties. Thus, insufficient feature extraction may hinder the fitting of tourist flow time series. To improve the prediction accuracy, a forecasting method combining the Boruta algorithm (Boruta), bidirectional long short-term memory (BiLSTM), and a convolutional neural network (CNN) is proposed in this study. The model was tested using a monthly tourist flow dataset of three adjacent tourist attractions (Hongcun Village, Mount Huangshan, and Xidi Village) in Anhui Province, China, from January 2011 to March 2021. Forecasting for Hongcun Village shows that the proposed model performs best in three-step-ahead forecasting with a root mean square error (RMSE) of 2341.77 and a mean absolute percentage error (MAPE) of 2.85 %. A comparative analysis demonstrated that the hybrid CNN-BiLSTM model outperformed benchmark models. Meanwhile, Boruta reduced the RMSE and MAPE of the model by 59.93 % and 54.11 %, respectively, which were superior to those of the baseline methods. Furthermore, a robustness test was applied considering Mount Huangshan and Xidi Village as additional exercises for prediction. The robustness test confirmed that the proposed model has the proper generalization capability. Accordingly, the forecasting framework used in this study is robust and operational. This can facilitate a new approach for tourism demand forecasting with SED and assist operators in making managerial decisions.