Economics and Business
Quarterly Reviews
ISSN 2775-9237 (Online)
Published: 07 February 2022
International Tourism Revenue Projections for Guilin in the Context of COVID-19
Jingming Jiang, Guangming Deng
Guilin University of Technology, China
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10.31014/aior.1992.05.01.404
Pages: 52-59
Keywords: International Tourism Revenue, Spearman Correlation Analysis, Multiple Regression Model, Support Vector Machine, Random Forest, COVID-19
Abstract
This paper collects relevant international tourism revenue data for Guilin from 2004 to 2020 for analysis and modelling using three algorithms, namely multiple linear regression model, support vector machine and random forest, to explore the variables affecting international tourism revenue in Guilin and to make model predictions for international tourism revenue from 2019 to 2020. The empirical evidence shows that the multiple linear regression model predicts the best results, especially the accurate prediction of the sharp decline in international tourism revenue when the new crown pneumonia epidemic spreads in 2020 , which can provide some scientific basis tourism development planning of Guilin city in the future.
References
Xia,. J. C., & Feng, X. X.(2020). Impact of novel coronavirus pneumonia on tourism industry and Countermeasures. China Business and Market(03),3-10. doi:10.14089/j.cnki.cn11-3664/f.2020.03.001.
Feng, Z. L.(2021). Strategies for revitalizing Guilin's tourism economy. Cooperative economy and Technology, (23):18-20.doi:10.13665/j.cnki.hzjjykj.2021.23.006.
Zhou, J. H., & Lu, P. (2019). empirical study on the factors influencing tourism income in Guilin. Journal of Guangxi Normal College (Philosophy and Social Science Edition), 40(02):84-92. doi:10.16601/j.cnki.issn1002-5227.2019.02.015.
Li, H., Wang,Y., & Yin, H.(2013).Forecasting and analysis of tourism demand in Guilin based on grey system theory. Henan Science, 31(05):679-682. doi:10.13537/j.issn.1004-3918.2013.05.020.
Xu, Z. Shu. (2021). Predicting the impact of taxation on tax revenue in the "floor stall economy" - based on a multiple linear regression model. China Circulation Economy(09),165-168. doi:10.16834/j.cnki.issn1009-5292.2021.09.052.
Foreman, N., Hesse, A., &Lundstrom, C.(2021). Machine Learning Fails To Improve Marathon Time Prediction Compared To Multiple Linear Regression: 161. Medicine & Science in Sports & Exercise 53: doi:10.1249/01.MSS.0000759656.97049.16.
Zhang, X. G.(2000). On statistical learning theory and support vector machines. Acta Automatica Sinica, (01):36-46. doi:10.16383/j.aas.2000.01.005.
Pontil, M. & Verri, A.(1998). Properties of support vector machines. Neural computation, 10(4):
Kapsiani, S.& Howlin, B.J.(2021).Random forest classification for predicting lifespan-extending chemical compounds.. Scientific reports(1), doi:10.1038/S41598-021-93070-6.
Samuel, B. & Lyerly.(1952).The average spearman rank correlation coefficient. Psychometrika(4), doi:10.1007/BF02288917.