论文标题
一种新的分解合奏方法，以预测旅游需求：主要来源国家的证据
A New Decomposition Ensemble Approach for Tourism Demand Forecasting: Evidence from Major Source Countries
论文作者
论文摘要
亚洲太平洋地区是世界上主要的国际旅游需求市场，其旅游需求受到各种因素的深远影响。先前的研究表明，不同的市场因素会影响不同时间尺度的旅游市场需求。因此，提出了分解合奏学习方法来分析不同市场因素对市场需求的影响，并进一步探讨了拟议方法对预测亚太地区旅游需求的潜在优势。这项研究仔细探讨了旅游目的地与主要来源国家之间的多尺度关系，通过分解相应的每月旅游到达，并具有噪音辅助的多元经验模式分解。以中国和马来西亚为案例研究，其各自的经验结果表明，分解集合方法明显优于包括统计模型，机器学习和深度学习模型的基准，就预测准确性和方向预测准确性而言。
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.
