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摘要翻译：
亚太地区是全球主要的国际旅游需求市场，其旅游需求深受各种因素的影响。以往的研究表明，不同的市场因素在不同的时间尺度上影响着旅游市场需求。在此基础上，提出了分解集成学习方法来分析不同市场因素对市场需求的影响，并进一步探讨了该方法在亚太地区旅游需求预测中的潜在优势。本研究以噪声辅助的多元经验模式分解方法，对旅游目的地与主要客源国的月接待量进行分解，以探讨旅游目的地与主要客源国之间的多尺度关系。分别以中国和马来西亚为例进行实证研究，结果表明分解集成方法在水平预测精度和方向预测精度方面明显优于统计模型、机器学习和深度学习模型等基准模型。
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英文标题：
《A New Decomposition Ensemble Approach for Tourism Demand Forecasting:
Evidence from Major Source Countries》
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作者：
Chengyuan Zhang and Fuxin Jiang and Shouyang Wang and Shaolong Sun
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最新提交年份：
2020
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分类信息：
一级分类：Economics 经济学
二级分类：General Economics 一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance 数量金融学
二级分类：Economics 经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要：
The Asian-pacific region is the major international tourism demand market in the world, and its tourism demand is deeply affected by various factors. Previous studies have shown that different market factors influence the tourism market demand at different timescales. Accordingly, the decomposition ensemble learning approach is proposed to analyze the impact of different market factors on market demand, and the potential advantages of the proposed method on forecasting tourism demand in the Asia-pacific region are further explored. This study carefully explores the multi-scale relationship between tourist destinations and the major source countries, by decomposing the corresponding monthly tourist arrivals with noise-assisted multivariate empirical mode decomposition. With the China and Malaysia as case studies, their respective empirical results show that decomposition ensemble approach significantly better than the benchmarks which include statistical model, machine learning and deep learning model, in terms of the level forecasting accuracy and directional forecasting accuracy.
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PDF链接：
https://arxiv.org/pdf/2002.09201
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