A novel deep learning approach for tourism volume forecasting with tourist search data

Abstract Tourism volume forecasting is the hot topic in tourism management, and deep learning techniques as the promising tool are becoming popular for capturing the characteristics of tourism volume data, which is reflected in two aspects: data dimension reduction (i.e., stacked autoencoders [SAE]) and model forecasting (i.e., bi‐directional gated recurrent unit neural network [Bi‐GRU]). With Hong Kong inbound arrivals as a case, this study has empirically verified that deep learning techniques can improve forecasting accuracy. Furthermore, the proposed approach (i.e., SAE‐Bi‐GRU) is significantly superior to benchmark models (i.e., PCA‐Bi‐GRU with Baidu index and Google trends).