Application of COEMD-S-SVR model in tourism demand forecasting and economic behavior analysis: The case of Sanya City

Abstract

Tourism industry played an increasingly prominent role in the socio-economic development in China. Therefore, it is of great significance to forecast the tourism demand, to analyze the development tendency of tourism, to explore the mode of economic linkage, and eventually to reveal the development regulation of tourism industry. In this paper, the empirical mode decomposition, the support vector regression, and the error factor adjustment were combined to forecast the tourism demand of Sanya City. The results demonstrate that the proposed model is more accurate than other models. Meanwhile, this paper also provides the insight analyses of the economic behavior through the tourism demand’s rectangular-ambulatory matrix. The analyses reveal the regulation of tourism industry and the future benefits of Sanya’s tourism.

Keywords:

• Tourism demand forecasting
• economic behavior analysis
• empirical mode decomposition (EMD)
• support vector regression (SVR)
• error factor adjustment
• rectangular-ambulatory matrix

Data availability statement

The data that support the findings of this study are openly available in Sanya Municipal Bureau of Statistics at http://tjj.sanya.gov.cn/tjjsite/tjgb/list2.shtml.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Number of tourists imply the statistics of the number of tourists visiting scenic spots. The concept of visitor times is “the total number of people including recurring people in a certain activity per unit time (such as visits and receptions)”. It is one of the indicators for calculating the capacity of scenic spots and the basis for scenic spots construction and management. Statistics can be calculated according to the calibration time or by day, month, quarter and year. The statistics are calculated on a monthly basis.

Additional information

Funding

Guo-Feng Fan thanks the support from the project grants: Science and Technology of Henan Province of China (No. 182400410419), the Academic and Technical Leader of Pingdingshan University, and Program for Young Scholar of Pingdingshan University. Wei-Chiang Hong thanks the support from Jiangsu Normal University, China (No. 9213618401).