Decomposition Methods for Tourism Demand Forecasting: A Comparative Study

Author:

Zhang Chengyuan1ORCID,Li Mingchen23,Sun Shaolong4ORCID,Tang Ling5,Wang Shouyang236ORCID

Affiliation:

1. School of Economics and Management, Xidian University, Xi’an, Shaanxi, China

2. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

3. School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China

4. School of Management, Xi’an Jiaotong University, Xi’an, China

5. School of Economics and Management, Beihang University, Beijing, China

6. Center for Forecasting Science, Chinese Academy of Sciences, Beijing, China

Abstract

Decomposition methods are extensively used for processing the complex patterns of tourism demand data. Given tourism demand data’s intrinsic complexity, it is critical to theoretically understand how different decomposition methods provide solutions. However, a comprehensive comparison of decomposition methods in tourism demand forecasting is still lacking. Hence, this study systematically investigates the forecasting performance of decomposition methods in tourism demand. Nine popular decomposition methods and six forecasting methods are employed, and their forecasting performance is compared. With Hong Kong visitor arrivals from eight major sources as a sample, three main conclusions are obtained from empirical results. First, all the decomposition methods generally outperform benchmark at all horizons, in both the level and directional forecasting. Second, decomposition methods can be divided into four categories based on forecasting accuracy. Finally, variational mode decomposition method is consistently superior to other eight decomposition methods and can provide the best forecasts in all cases.

Funder

National Natural Science Foundation of China

fundamental research funds for the central universities

Publisher

SAGE Publications

Subject

Tourism, Leisure and Hospitality Management,Transportation,Geography, Planning and Development

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