摘要翻译：
准确地预测游客到达的季节和趋势是一项非常具有挑战性的任务。鉴于游客到达量的季节性和趋势预测的重要性，以往的研究工作对此关注有限。本文提出了一种基于变分模式分解(VMD)和最小二乘支持向量回归(LSSVR)的自适应多尺度集成(AME)学习方法，用于短期、中期和长期旅游接待量的季节性和趋势预测。在我们开发的AME学习方法的制定中，最初的游客到达序列首先被分解为趋势、季节性和剩余波动性成分。然后，用ARIMA预测趋势分量，SARIMA预测12个月周期的季节性分量，LSSVR预测剩余波动分量。最后，利用基于LSSVR的非线性集成方法对三个分量的预测结果进行集成，生成游客到达量的集成预测。此外，采用直接预测策略实现多步提前预测。通过两个精度测度和Diebold-Mariano检验，实证结果表明，与本研究中使用的其他基准相比，我们提出的AME学习方法可以获得更高的水平和方向预测精度，表明我们提出的方法是一个有希望的预测季节性和波动性较高的游客到达量的模型。
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英文标题：
《Seasonal and Trend Forecasting of Tourist Arrivals: An Adaptive
  Multiscale Ensemble Learning Approach》
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作者：
Shaolong Suna, Dan Bi, Ju-e Guo, Shouyang Wang
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最新提交年份：
2020
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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 accurate seasonal and trend forecasting of tourist arrivals is a very challenging task. In the view of the importance of seasonal and trend forecasting of tourist arrivals, and limited research work paid attention to these previously. In this study, a new adaptive multiscale ensemble (AME) learning approach incorporating variational mode decomposition (VMD) and least square support vector regression (LSSVR) is developed for short-, medium-, and long-term seasonal and trend forecasting of tourist arrivals. In the formulation of our developed AME learning approach, the original tourist arrivals series are first decomposed into the trend, seasonal and remainders volatility components. Then, the ARIMA is used to forecast the trend component, the SARIMA is used to forecast seasonal component with a 12-month cycle, while the LSSVR is used to forecast remainder volatility components. Finally, the forecasting results of the three components are aggregated to generate an ensemble forecasting of tourist arrivals by the LSSVR based nonlinear ensemble approach. Furthermore, a direct strategy is used to implement multi-step-ahead forecasting. Taking two accuracy measures and the Diebold-Mariano test, the empirical results demonstrate that our proposed AME learning approach can achieve higher level and directional forecasting accuracy compared with other benchmarks used in this study, indicating that our proposed approach is a promising model for forecasting tourist arrivals with high seasonality and volatility.
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PDF链接：
https://arxiv.org/pdf/2002.08021