Before revision: The large amount of tourism-related data presents a series of challenges for tourism demand forecasting, including data deficiencies, multicollinearity and long calculation time. A Bagging-based multivariate ensemble deep learning model, integrating Stacked Autoencoders and KELM (B-SAKE) is proposed to address these challenges in this study.
After revision: The large amount of tourism-related data presents a series of challenges for tourism demand forecasting, including data deficiencies, multicollinearity and long calculation times. A bagging-based multivariate ensemble deep learning model, integrating Stacked Autoencoders and KELM (B-SAKE) is proposed to address these challenges in this study.