Before revision:  Moreover, the forecasting performance of the models is evaluated with consistent results by means of the multi-step-ahead forecasting scheme .
After revision:  The ensemble deep learning model we propose contributes to tourism forecasting literature and benefits relevant government officials and tourism practitioners .