  The availability of tourism-related big data increases the potential to
improve the accuracy of tourism demand forecasting, but presents significant
challenges for forecasting, including curse of dimensionality and high model
complexity. A novel bagging-based multivariate ensemble deep learning approach
integrating stacked autoencoders and kernel-based extreme learning machines
(B-SAKE) is proposed to address these challenges in this study. By using
historical tourist arrival data, economic variable data and search intensity
index (SII) data, we forecast tourist arrivals in Beijing from four countries.
The consistent results of multiple schemes suggest that our proposed B-SAKE
approach outperforms benchmark models in terms of level accuracy, directional
accuracy and even statistical significance. Both bagging and stacked
autoencoder can effectively alleviate the challenges brought by tourism big
data and improve the forecasting performance of the models. The ensemble deep
learning model we propose contributes to tourism forecasting literature and
benefits relevant government officials and tourism practitioners.
