IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v8y2016i10p1037-d80634.html
   My bibliography  Save this article

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

Author

Listed:
  • Hoang-Sa Dang

    (Department of Industrial Engineering and Management, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung road, Sanmin district, Kaohsiung City 80778, Taiwan)

  • Ying-Fang Huang

    (Department of Industrial Engineering and Management, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung road, Sanmin district, Kaohsiung City 80778, Taiwan)

  • Chia-Nan Wang

    (Department of Industrial Engineering and Management, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung road, Sanmin district, Kaohsiung City 80778, Taiwan)

  • Thuy-Mai-Trinh Nguyen

    (Department of Industrial Engineering and Management, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung road, Sanmin district, Kaohsiung City 80778, Taiwan)

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.

Suggested Citation

  • Hoang-Sa Dang & Ying-Fang Huang & Chia-Nan Wang & Thuy-Mai-Trinh Nguyen, 2016. "An Application of the Short-Term Forecasting with Limited Data in the Healthcare Traveling Industry," Sustainability, MDPI, vol. 8(10), pages 1-14, October.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:10:p:1037-:d:80634
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/8/10/1037/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/8/10/1037/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ying-Fang Huang & Chia-Nan Wang & Hoang-Sa Dang & Shun-Te Lai, 2015. "Predicting the Trend of Taiwan’s Electronic Paper Industry by an Effective Combined Grey Model," Sustainability, MDPI, vol. 7(8), pages 1-20, August.
    2. Snyder, Ralph D. & Koehler, Anne B. & Ord, J. Keith, 2002. "Forecasting for inventory control with exponential smoothing," International Journal of Forecasting, Elsevier, vol. 18(1), pages 5-18.
    3. Ya-Ling Huang, 2012. "Forecasting the demand for health tourism in Asian countries using a GM(1,1)-Alpha model," Tourism and Hospitality Management, University of Rijeka, Faculty of Tourism and Hospitality Management, vol. 18(2), pages 171-181, December.
    4. Marasco, A. & Picucci, A. & Romano, A., 2016. "Market share dynamics using Lotka–Volterra models," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 49-62.
    5. P. J. Harrison, 1967. "Exponential Smoothing and Short-Term Sales Forecasting," Management Science, INFORMS, vol. 13(11), pages 821-842, July.
    6. Teng, Hsi-che & Huang, Ying-fang, 2013. "The use for the competition theory of the industrial investment decisions—a case study of the Taiwan IC assembly industry," International Journal of Production Economics, Elsevier, vol. 141(1), pages 335-338.
    7. Wijeratne, A.W. & Yi, Fengqi & Wei, Junjie, 2009. "Bifurcation analysis in the diffusive Lotka–Volterra system: An application to market economy," Chaos, Solitons & Fractals, Elsevier, vol. 40(2), pages 902-911.
    8. Lin, Chiun-Sin, 2013. "Forecasting and analyzing the competitive diffusion of mobile cellular broadband and fixed broadband in Taiwan with limited historical data," Economic Modelling, Elsevier, vol. 35(C), pages 207-213.
    9. A.W. Wijeratne & J.A. Karunaratne & Chuncheng Wang & Junjie Wei, 2008. "An empirical Lotka-Volterra system for competition dynamics in the telecommunication sector of Sri Lanka," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 3(5), pages 482-497.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gatabazi, P. & Mba, J.C. & Pindza, E. & Labuschagne, C., 2019. "Grey Lotka–Volterra models with application to cryptocurrencies adoption," Chaos, Solitons & Fractals, Elsevier, vol. 122(C), pages 47-57.
    2. Goran Dominioni & Addolorata Marasco & Alessandro Romano, 2018. "A mathematical approach to study and forecast racial groups interactions: deterministic modeling and scenario method," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(4), pages 1929-1956, July.
    3. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    4. Wesley Marcos Almeida & Claudimar Pereira Veiga, 2023. "Does demand forecasting matter to retailing?," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 219-232, June.
    5. Marco Desogus & Beatrice Venturi, 2023. "Stability and Bifurcations in Banks and Small Enterprises—A Three-Dimensional Continuous-Time Dynamical System," JRFM, MDPI, vol. 16(3), pages 1-20, March.
    6. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
    7. Mirko Kremer & Enno Siemsen & Douglas J. Thomas, 2016. "The Sum and Its Parts: Judgmental Hierarchical Forecasting," Management Science, INFORMS, vol. 62(9), pages 2745-2764, September.
    8. Lin Zhuo & Xiangfeng Guan & Songzhong Ye, 2020. "Quantitative Evaluation and Prediction Analysis of the Healthy and Sustainable Development of China’s Sports Industry," Sustainability, MDPI, vol. 12(6), pages 1-16, March.
    9. Janssen, E. & Strijbosch, L.W.G. & Brekelmans, R.C.M., 2007. "How to Determine the Order-up-to Level When Demand is Gamma Distributed with Unknown Parameters," Discussion Paper 2007-71, Tilburg University, Center for Economic Research.
    10. Merigó, José M. & Palacios-Marqués, Daniel & Ribeiro-Navarrete, Belén, 2015. "Aggregation systems for sales forecasting," Journal of Business Research, Elsevier, vol. 68(11), pages 2299-2304.
    11. Wang, Zhi, 2003. "WTO accession, the "Greater China" free-trade area, and economic integration across the Taiwan Strait," China Economic Review, Elsevier, vol. 14(3), pages 316-349.
    12. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.
    13. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    14. Wong, W.K. & Guo, Z.X., 2010. "A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm," International Journal of Production Economics, Elsevier, vol. 128(2), pages 614-624, December.
    15. Chia-Nan Wang & Hong-Xuyen Thi Ho & Shih-Hsiung Luo & Tsung-Fu Lin, 2017. "An Integrated Approach to Evaluating and Selecting Green Logistics Providers for Sustainable Development," Sustainability, MDPI, vol. 9(2), pages 1-21, February.
    16. Hill, Arthur V. & Zhang, Weiyong & Burch, Gerald F., 2015. "Forecasting the forecastability quotient for inventory management," International Journal of Forecasting, Elsevier, vol. 31(3), pages 651-663.
    17. A A Syntetos & J E Boylan & S M Disney, 2009. "Forecasting for inventory planning: a 50-year review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 149-160, May.
    18. Chiara Sotis, 2021. "How do Google searches for symptoms, news and unemployment interact during COVID-19? A Lotka–Volterra analysis of google trends data," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(6), pages 2001-2016, December.
    19. Godfrey, Gregory A. & Powell, Warren B., 2000. "Adaptive estimation of daily demands with complex calendar effects for freight transportation," Transportation Research Part B: Methodological, Elsevier, vol. 34(6), pages 451-469, August.
    20. Toly Chen, 2016. "Competitive and Sustainable Manufacturing in the Age of Globalization," Sustainability, MDPI, vol. 9(1), pages 1-5, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:8:y:2016:i:10:p:1037-:d:80634. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.