摘要翻译：
旅游大数据的存在为提高旅游需求预测的准确性增加了潜力，但也给预测带来了极大的挑战，包括维数诅咒和模型复杂度高等问题。针对这些问题，本文提出了一种基于Bagging的多变量集成深度学习方法，该方法集成了堆叠自动编码器和基于核的极限学习机（B-SACE）。利用历史旅游数据、经济变量数据和搜索强度指数(SII)数据，对北京四个国家的旅游客流量进行了预测。多个方案的一致结果表明，我们提出的B-SACE方法在水平精度、方向精度甚至统计意义方面都优于基准模型。bagging和stacked autoencoder都能有效缓解旅游大数据带来的挑战，提高模型的预测性能。我们提出的集成深度学习模型有助于旅游预测文献，并惠及相关政府官员和旅游从业者。
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英文标题：
《Tourism Demand Forecasting: An Ensemble Deep Learning Approach》
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作者：
Shaolong Sun, Yanzhao Li, Ju-e Guo, Shouyang Wang
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最新提交年份：
2021
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  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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PDF链接：
https://arxiv.org/pdf/2002.07964