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Neural Computing and Applications

, Volume 31, Supplement 2, pp 1075–1089 | Cite as

Optimizing mathematical parameters of Grey system theory: an empirical forecasting case of Vietnamese tourism

  • Nhu-Ty NguyenEmail author
  • Thanh-Tuyen Tran
Original Article

Abstract

Accurately forecasting the demand for international and domestic tourism is a key goal for tourism industry leaders. The purpose of this study is to present more appropriate models for forecasting the demand for tourism in Vietnam. The authors apply GM(1,1), Verhulst, DGM(1,1) and DGM(2,1) to test which concise prediction models can improve the ability to predict the number of tourists visiting this country. In order to guarantee the accuracy of forecasting process, data cover in the period from 2005 through 2013 and are obtained from the official website of VNATR “Vietnam National Administration of Tourism” report. The MAPE, MSE, RMSE and MAD are four important criteria which are used to compare the various forecasting models results. Key findings indicate that the optimal value of GM(1,1), Verhulst, DGM(1,1) can enhance the forecasting results perfectly with minimum predicted errors. In the case of the tourism revenue, using the Verhulst model is evidently better than the others. For the number of international and domestic tourist prediction, the application of Verhulst and DGM(1,1) models is well done. For visitors coming from specific countries (i.e., China, Korea, Taiwan, Japan and America), DGM(2,1) is very poor for predicting in this situation, whereas remaining three models GM(1,1), Verhulst, DGM(1,1) and DGM(2,1) perform excellently. The results also pointed out that the tourism demands in Vietnam are growing rapidly; thus, the governments must be well prepared for tourism industry and enhance relative fundamental construction for tourism markets.

Keywords

Vietnamese tourism GM(1,1) Verhulst DGM(1,1) DGM(2,1) Forecasting 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.School of BusinessInternational University – Vietnam National University HCMC; Quarter 6Ho Chi Minh CityVietnam
  2. 2.Scientific Research OfficeLac Hong UniversityBien HoaVietnam

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