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Sustainability 2016, 8(10), 1037; doi:10.3390/su8101037

An Application of the Short-Term Forecasting with Limited Data in the Healthcare Traveling Industry

Department of Industrial Engineering and Management, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung road, Sanmin district, Kaohsiung City 80778, Taiwan
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Author to whom correspondence should be addressed.
Academic Editor: Marc A. Rosen
Received: 3 July 2016 / Revised: 14 September 2016 / Accepted: 9 October 2016 / Published: 16 October 2016
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Abstract

In real practice, forecasting under the limited data has attracted more attention in business activities, especially in the healthcare traveling industry in its current stage. However, there are only a few research studies focusing on this issue. Thus, the purposes of this paper were to determine the forecasted performance of several current forecasting methods as well as to examine their applications. Taking advantage of the small data requirement for model construction, three models including the exponential smoothing model, the Grey model GM(1,1), and the modified Lotka-Volterra model (L.V.), were used to conduct forecasting analyses based on the data of foreign patients from 2001 to 2013 in six destinations. The results indicated that the L.V. model had higher prediction power than the other two models, and it obtained the best forecasting performance with an 89.7% precision rate. In conclusion, the L.V. model is the best model for estimating the market size of the healthcare traveling industry, followed by the GM(1,1) model. The contribution of this study is to offer a useful statistical tool for short-term planning, which can be applied to the healthcare traveling industry in particular, and for other business forecasting under the conditions of limited data in general. View Full-Text
Keywords: forecasting; limited data; healthcare traveling industry forecasting; limited data; healthcare traveling industry
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MDPI and ACS Style

Dang, H.-S.; Huang, Y.-F.; Wang, C.-N.; Nguyen, T.-M.-T. An Application of the Short-Term Forecasting with Limited Data in the Healthcare Traveling Industry. Sustainability 2016, 8, 1037.

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