Seasonal and trend forecasting of tourist arrivals: An adaptive multiscale ensemble learning approach. (28th January 2022)

• Record Type: Journal Article
• Authors: Xing, Guangyuan; Sun, Shaolong; Bi, Dan; Guo, Ju‐e; Wang, Shouyang
• Is Part Of: International journal of tourism research. Volume 24:Number 3(2022)
• Page Start: 425
• Page End: 442
• Publication Date: 2022-01-28
• Subjects: ensemble learning -- least square support vector regression -- seasonality -- tourism demand forecasting -- variational mode decomposition; Tourism -- Periodicals; 338.4791
• DOI: 10.1002/jtr.2512
• Languages: English
• ISSNs: 1099-2340

Abstract

Abstract: In this study, we propose an adaptive multiscale ensemble (AME) learning approach, which consists of variational mode decomposition (VMD) and least square support vector regression (LSSVR) for seasonal and trend forecasting of tourist arrivals. In the formulation of AME learning approach, the original tourist arrival series is decomposed into the trend, seasonal, and remaining volatility components. Then, ARIMA is used to forecast the trend component, SARIMA is used to forecast the seasonal component, and LSSVR is used to forecast the remaining volatility components. The empirical results demonstrate that our proposed AME learning approach can achieve higher forecasting accuracy.