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 Volume 6, Issue 11 (November 2019), Pages: 42-54


 Original Research Paper

 Title: Forecasting Vietnamese tourists’ accommodation demand using grey forecasting and ARIMA models

 Author(s): Nhu-Ty Nguyen 1, Tuong-Thuy-Tran Nguyen 1, Thanh-Tuyen Tran 2, *


 1School of Business, International University – VNU-HCMC, Quarter 6, Linh Trung Ward, Thu Duc District, HCMC, Vietnam
 2Scientific Research Center, Lac Hong University, No.10 Huynh Van Nghe Street, Dong Nai Provinec, Vietnam

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 * Corresponding Author. 

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The development of the tourist accommodation sector significantly contributes to the overall growth of tourism. The need for accurate predicting the demand for tourist accommodation of international and domestic tourists is a key goal for future good preparation and appropriate strategy. The objective of this study is to show some Grey forecasting models involving GM (1, 1), Verhulst, DGM (1,1), and ARIMA models consist of ARIMA (0, 1, 1) for the projection of the future number of domestic and international visitors serviced by tourist accommodation establishments in Lam Dong province. The author of this study applies four essential criteria Mean absolute percentage error (MAPE), Mean absolute deviation (MAD), Mean square error (MSE), Root mean square error (RMSE) to compare the various forecasting models outcomes and to examine which suitable forecasting models can improve the capability to project the number of future international and domestic tourists served by tourist accommodations in Lam Dong province. The monthly statistics of number tourists serviced of tourist accommodation and total revenue from tourist accommodation service in Lam Dong province covering in the period from January 2012 to October 2018 are obtained from the official website of general statistics office of Lam Dong province and statistical yearbook of Lam Dong in order to guarantee the accuracy of forecasting procedure. The key findings of this study are that ARIMA (1, 1, 1) (1, 1, 1) model can effectively predict the number of domestic tourists with more accurate outcomes with a minimum predicted errors. Besides that, the number of international visitors serviced by tourist accommodation can be obtained more accurately by using the ARIMA (1, 1, 1) (1, 1, 1) model. In the case of total revenue from tourist accommodation service in Lam Dong province, ARIMA (0, 1, 1) (0, 1, 1), GM (1, 1), DGM (1, 1) models have better performance than the Verhulst model. The forecasting results also showed the number of international and domestic tourists serviced by tourist accommodation in Lam Dong is growth slightly. Therefore, Lam Dong Authority must make good preparation and appropriate strategies to response exactly at any changes and supply for tourist accommodation markets. 

 © 2019 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (

 Keywords: GM (1, 1), Verhulst, DGM (1, 1), ARIMA, Forecasting, Grey system

 Article History: Received 28 November 2018, Received in revised form 29 August 2019, Accepted 3 September 2019


No Acknowledgement.

 Compliance with ethical standards

 Conflict of interest:  The authors declare that they have no conflict of interest.


 Nguyen NT, Nguyen TTT, and Tran TT (2019). Forecasting Vietnamese tourists’ accommodation demand using grey forecasting and ARIMA modelss. International Journal of Advanced and Applied Sciences, 6(11): 42-54

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 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 


 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 


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