Previous studies have shown that different market factors influence tourism demand at different timescales. Accordingly, we propose the decomposition ensemble learning approach to analyze impact of different market factors on tourism demand, and explore the potential advantages of the proposed method on forecasting tourism demand in Asia-Pacific region. By decomposing tourist arrivals with noise-assisted multivariate empirical mode decomposition, this study further explores the multiscale relationship between tourist destinations and major source countries. The empirical results show that decomposition ensemble approach performs significantly better than benchmarks in terms of the level forecasting accuracy and directional forecasting accuracy.

中文翻译：

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一种新的旅游需求预测分解集成方法：来自亚太地区主要客源国的证据

以往的研究表明，不同的市场因素会在不同的时间尺度上影响旅游需求。因此，我们提出分解集成学习方法来分析不同市场因素对旅游需求的影响，并探索该方法在预测亚太地区旅游需求方面的潜在优势。本研究通过噪声辅助多元经验模态分解来分解游客人数，进一步探讨了旅游目的地与主要客源国之间的多尺度关系。实证结果表明，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基准。