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 approach integrating stacked autoencoders and kernel-based extreme learning machines (B-SAKE) is proposed to address these challenges in this study. We forecast tourist arrivals in Beijing from four countries by adopting historical data on tourist arrivals in Beijing, economic indicators and online tourist behavior variables. The results from the cases of four origin countries suggest that our proposed B-SAKE approach outperforms than benchmark models in terms of horizontal accuracy, directional accuracy and  statistical significance. Both bagging and stacked autoencoder can  improve the forecasting performance of the models. Moreover, the forecasting performance of the models is evaluated with consistent results by means of the multi-step-ahead forecasting scheme .
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 .
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including data deficiencies, multicollinearity and long calculation times. A
but presents significant challenges for forecasting, including curse of dimensionality and high model complexity. A novel
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By using historical tourist arrival data, economic variable data and search intensity index (SII) data, we
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by adopting historical data on tourist arrivals in Beijing, economic indicators and online tourist behavior variables. The results from the cases of four origin countries
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effectively alleviate the challenges brought by tourism big data and
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Moreover, the forecasting performance of the models is evaluated with consistent results by means of the multi-step-ahead forecasting scheme
The ensemble deep learning model we propose contributes to tourism forecasting literature and benefits relevant government officials and tourism practitioners