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A novel procedure for multimodel development using the grey silhouette coefficient for small-data-set forecasting

Author

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  • Che-Jung Chang

    (Ningbo University, Ningbo City, China
    Chung Yuan Christian University, Chung-Li City, Taiwan, ROC)

  • Wen-Li Dai

    (Tainan University of Technology, Tainan City, Taiwan, ROC)

  • Chien-Chih Chen

    (National Chen Kung University, Tainan City, Taiwan, ROC)

Abstract

Small-data-set forecasting problems are a critical issue in various fields, with the early stage of a manufacturing system being a good example. Manufacturers require sufficient knowledge to minimize overall production costs, but this is difficult to achieve due to limited number of samples available at such times. This research was thus conducted to develop a modelling procedure to assist managers or decision makers in acquiring stable prediction results from small data sets. The proposed method is a two-stage procedure. First, we assessed some single models to determine whether the tendency of a real sequence can be reflected using grey incidence analysis, and we then evaluated their forecasting stability based on the relative ratio of error range. Second, a grey silhouette coefficient was developed to create an applicable hybrid forecasting model for small samples. Two real cases were analysed to confirm the effectiveness and practical value of the proposed method. The empirical results showed that the multimodel procedure can minimize forecasting errors and improve forecasting results with limited data. Consequently, the proposed procedure is considered a feasible tool for small-data-set forecasting problems.

Suggested Citation

  • Che-Jung Chang & Wen-Li Dai & Chien-Chih Chen, 2015. "A novel procedure for multimodel development using the grey silhouette coefficient for small-data-set forecasting," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(11), pages 1887-1894, November.
  • Handle: RePEc:pal:jorsoc:v:66:y:2015:i:11:p:1887-1894
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    Citations

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    Cited by:

    1. Che-Jung Chang & Liping Yu & Peng Jin, 2016. "A mega-trend-diffusion grey forecasting model for short-term manufacturing demand," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(12), pages 1439-1445, December.
    2. Yi-Chung Hu, 2017. "Electricity consumption prediction using a neural-network-based grey forecasting approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1259-1264, October.
    3. Patrick Link & Miltiadis Poursanidis & Jochen Schmid & Rebekka Zache & Martin Kurnatowski & Uwe Teicher & Steffen Ihlenfeldt, 2022. "Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2129-2142, October.
    4. Wei Meng & Daoli Yang & Hui Huang, 2018. "Prediction of China’s Sulfur Dioxide Emissions by Discrete Grey Model with Fractional Order Generation Operators," Complexity, Hindawi, vol. 2018, pages 1-13, January.
    5. Yi-Chung Hu & Peng Jiang, 2017. "Forecasting energy demand using neural-network-based grey residual modification models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(5), pages 556-565, May.

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