Translated Titles

An improved non-equigap grey forecasting model



Key Words

灰預測 ; 非等間距 ; 小樣本 ; 時間數列 ; Non-equigap ; Grey forecasting ; Small data set ; Time series



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Chinese Abstract

世界經濟潮流導致經濟的競爭加劇,產品開始由大量規格化生產轉變為少量多樣 及客製化的生產,產品生命週期變的短暫且快速,在高投資成本的電子產業需求不再 是容易預測的長期線性趨勢,企業經營與獲利必須因應環境快速的變動做調整。在產 品生產初期由於成本考量及縮短開發階段時間,普遍只能獲得有限的資料,而傳統的 預測技術大多需要大量的資料分析再給予決策,已經無法符合現有的緊迫需求,因此 決策者在有限的樣本下做出迫切性判斷已是無法迴避,而小樣本資料的預測也變成重 要的分析工具。 灰預測模型為小樣本預測的重要方法之一,其中大多採用固定時間間距的建模方 式,而限制了模型的廣泛應用。本研究透過趨勢潛力追蹤法分析資料行為,以擷取非 等間距資料的隱含資訊找出趨勢潛力值,在灰色系統理論的架構下,發展出一種良好 適應性的灰預測模型,做為小樣本資料的分析預測工具。經過實例驗證,在本研究所 提出的方法能依照樣本特性建構適合的模型,更可同時成功地提高小樣本資料的預測 準確度。 關鍵字:灰預測、非等間距、小樣本、時間數列。

English Abstract

The trend of global economy increases industrial competition. Products experience great changes from mass production into customization and lean production. The life cycle of products is shortened and rapid. Demand is no longer a long-term linear trend easily forecasted in high cost electronics industry. Industrial management and profits are in need to adjust to the rapid change in the business nature. In the early stage of manufacturing, it is ordinary that only limited data can be obtained due to the consideration of cost and time spent on invention. However, traditional forecasting skills always require mass data for analysis before making any decisions, which cannot meet current urgent demands. As a result, it is unavoidable that decision makers are forced to make rush decisions with limited samples. Therefore, the forecasting of small data sets is being valued as a vital analytical method. Grey forecasting model is one of the important approaches of small-data-set forecasting. It always establishes models by fixing time span, which leads to the models’ limited application. This study analyzed data with trend and potency tracking method and discovered the generation of trend and potency value by collecting extra information of unequal span data. The study was aimed at developing an adaptive grey forecasting model under the scheme of grey system theory to serve as a forecasting tool of small-data-set analysis. Empirical evidence proved that the approach proposed in the study could not only establish adaptive models in accordance with the characteristics of samples but successfully improve the forecasting precision of small data sets. Key words: Grey forecasting, Non-equigap, Small data set, Time series

Topic Category 管理學院 > 工業與資訊管理學系碩士在職專班
工程學 > 工程學總論
社會科學 > 管理學
  1. Taiwan semiconductor industry production. Technological Forecasting & Social
  2. prediction. The 4th National Conference on Grey Theory and Applications, Kaohsiung,
  3. Chen, C. I., Application of the novel nonlinear grey Bernoulli model for forecasting
  4. Chen, H. S., Chang, W. C., A study of optimal grey model GM(1,1). Journal of Grey
  5. Conference on Grey Theory and Applications, Yunlin, Taiwan, A26-A31, 2001. (in
  6. Cheng, K. H., Shah, H. C., A new method for earthquake forecasting using gery theory:
  7. Deng, J. L., Control Problems of Grey Systems. Systems and Control Letters, 1(5),
  8. 288-294, 1982.
  9. Deng, J. L., Introduction to Grey System Theory. The Journal of Grey System, 1(1), 1-24,
  10. He, Y., Bao, Y. D., Grey-Markov forecasting model and its application. Systems
  11. Hsin, J. Y., Tsai, Y. P., The research of superposition method for α value in grey
  12. Hsu, C. C., Chen, C. Y., Applications of improved grey prediction model for power
  13. demand forecasting. Energy Conversion and Management, 44(14), 2241-2249, 2003a.
  14. Hsu, C. C., Chen, C. Y., A modified Grey forecasting model for long-term prediction.
  15. Journal of the Chinese Institute of Engineers, 26(3), 301-308, 2003b.
  16. Hsu, C. I., Wen, Y. H., Improved grey prediction models for the trans-pacific air passenger
  17. market. Transportation Planning and Technology, 22(2), 87-107, 1998.
  18. Hsu, L. C., Applying the Grey prediction model to the global integrated ciruit industry.
  19. Technological Forecasting & Social Change, 70(6), 563-574, 2003.
  20. Huang, C., Principle of information diffusion. Fuzzy Sets and Systems, 91(1), 69-90, 1997.
  21. Huang, C., Moraga, C., A diffusion-neural-network for learning from small samples.
  22. International Journal of Approximate Reasoning, 35(2), 137-161, 2004.
  23. Jang, J. S. R., ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions
  24. on Systems, Man and Cybernetics, 23(3), 665-685, 1993.
  25. service life of mine-vehicle engine cylinder wall by using of non-equidistance grey
  26. Li, D. C., Chen, L. S., Lin Y. S., Using functional virtual population as assistance to learn
  27. Li, D. C., Lin, Y. S., Using virtual sample generation to build up management knowledge
  28. Li, D. C., Wu, C. S., Tsai, T. I., Chang, F. M., Using mega-fuzzification and data trend
  29. estimation in small data set learning for early FMS scheduling knowledge. Computer
  30. & Operations Research, 33(6), 1857-1869, 2006b.
  31. samples in small data set learning for early flexible manufacturing system scheduling
  32. knowledge. Computer & Operations Research, 34(4), 966-982, 2007.
  33. Li, D. C., Yeh C. W., A non-parametric learning algorithm for small manufacturing data
  34. sets. Expert Systems with Applications, 34(1), 391-398, 2008.
  35. Lin, C. T., Yang, S. Y., Forecast of the output value of Taiwan’s opto-electronics industry
  36. using the Grey forecasting model. Technological Forecasting & Social Change, 70(2),
  37. Liu, S. F., Dang, Y. G., Fang, Z. G., The Theory of Grey System and Its Applications.
  38. Crashworthiness, 10(6), 635-642, 2005.
  39. Mao, M., Chirwa, E. C., Application of grey model GM(1,1) to vehicle fatality risk
  40. estimation. Technological Forecasting & Social Change, 73(5), 588-605, 2006.
  41. Niyogi, P., Girosi, F., Poggin, T., Incorporating prior information in machine learning by
  42. Pan, C. L., Huang, Y. F., Lin, G., The study on α algorithm of grey prediction Z (1) (k)
  43. Tan, G.. J., The structure method and application of background value in grey system
  44. 1st National Conference on Grey Theory and Applications, Kaohsiung, Taiwan,
  45. Zhou, P., Ang, B. W., Poh K. L., A trigonometric grey prediction approach to forecasting
  46. Chang, C. J., Adaptive grey forecasting model. Master’s thesis, National Cheng Kung
  47. University, Tainan, Taiwan, 2008.
  48. Chang, S. C., Lai, H. C., Yu, H. C., A variable P value rolling grey forecasting model for
  49. Change, 72(5), 623-640, 2005.
  50. Chang, S. C., Wu, J. H., Lee, C. T., A study on the characteristics of α(k) of grey
  51. Taiwan, 291-296, 1999. (in Chinese)
  52. unemployment rate. Chaos, Solitons and Fractals, 37(1), 278-287, 2006.
  53. System, 1(2), 141-145, 1998. (in Chinese)
  54. Chen, K. W., Lai, C. J., Optimal fixed α in Z (1) (k) for GM(1,1). The 6th National
  55. Chinese)
  56. Application to California. The Journal of Grey System, 11(3), 293-302, 1999.
  57. Dai, W. I., Li, J. F., Modeling research on non-equidistance GM(1,1) model. Systems
  58. Engineer-Theory & Practice, 25(9), 89-93, 2005. (in Chinese)
  59. Deng, J. L., Grey System Fundamental Method. Huazhong University of Science and
  60. Technology Press, Wuhan, China, 1987. (in Chinese)
  61. 1989.
  62. Deng, J. L., A novel GM(1,1) model for non-equigap series. The Journal of Grey System,
  63. 9(2), 111-116, 1997.
  64. He, X. G., Sun, G. Z., A non-equigap grey model NGM(1,1). The Journal of Grey System,
  65. 13(2), 189-192, 2001.
  66. Engineer-Theory & Practice, 12(4), 59-63, 1992. (in Chinese)
  67. forecasting. The 5th National Conference on Grey Theory and Applications, Taipei,
  68. Taiwan, 305-308, 2000. (in Chinese)
  69. Huang, S. X., Li, Z. C., Grey modeling of non-equidistant data sequent for forecasting
  70. subsidence of engineering buildings. Geospatial Information, 2(1), 41-43, 2004. (in
  71. Chinese)
  72. Jiang, S. S., E, J. Q., Li, J., Zhang, H. M., Gong, J. K., Yuan, W. H., Prediction on residual
  73. forecasting model. Goal Mine Machinery, 29(7), 69-71, 2008. (in Chinese)
  74. scheduling knowledge in dynamic manufacturing environments. International Journal
  75. of Production Research, 41(17), 4011-4024, 2003.
  76. Li, D. C., Wu, C. S., Chang, F. M., Using data-fuzzification technology in small data set
  77. learning to improve FMS scheduling accuracy. International Journal of Advanced
  78. Manufacturing Technology, 27(3-4), 321-328, 2005.
  79. in the early manufacturing stage. European Journal of Operational Research, 175(1),
  80. 413-434, 2006a.
  81. Li, D. C., Wu, C. S., Tsai, T. I., Lina, Y. S., Using mega-trend-diffusion and artifical
  82. Li, Y. G., Li, Q. F., Zhao, G. F., An improvement on Grey forecasting model. System
  83. Engineering, 10(6), 27-31, 1992. (in Chinese)
  84. 177-186, 2003.
  85. Lin, C. T., Yeh, H. Y., The use of grey prediction to forecast Taiwan stock index option
  86. prices. The Journal of Grey System, 18(4), 381-390, 2006.
  87. Science Press, Beijing, China, 2004. (in Chinese)
  88. Liu, W. G., Jiang, L. H., Grey GM(1,1) model in ferroalloy burdening. Iron and Steel,
  89. 31(9), 52-56, 1996. (in Chinese)
  90. Lu, H. C., Universal GM(1,1) model based on data mapping concept. The Journal of Grey
  91. System, 8(4), 307-319, 1996.
  92. Luo, D., Liu, S. F., Dang, Y. G., The optimization of grey model GM(1,1). Engineering
  93. Science, 5(8), 50-53, 2003. (in Chinese)
  94. Luo, E. X., Qian, X. S., Li, R., Construction and empirical research of the variable
  95. parameter value rolling grey forecasting model. Journal of University of Shanghai for
  96. Science and Technology, 28(5), 465-468, 2006. (in Chinese)
  97. Mao, M., Chirwa, E. C., Combination of grey model GM(1,1) with three-point moving
  98. average for accurate vehicle fatality risk prediction. International Journal of
  99. creating virtual examples. Proceedings of the IEEE, 86(11), 2196-2209, 1998.
  100. with iterative method as basis. The 7th National Conference on Grey Theory and
  101. Applications, Tainan, Taiwan, I27-I32, 2002.
  102. Sheng, J. G., Improvement on and applying of GM(1,1). Mathematics in Practice and
  103. Theory, 30(3), 10-15, 1990. (in Chinese)
  104. Shi, B. Z., Modeling of non-equigap GM(1,1). The Journal of Grey System, 5(2), 105-113,
  105. 1993.
  106. Song, Z. M., Tong, X. J. Xiao, X. P., Center approach grey GM(1,1) model. Systems
  107. Engineer-Theory & Practice, 21(5), 110-113, 2001. (in Chinese)
  108. Sun, G., Prediction of vegetable yields by grey model GM(1,1). The Journal of Grey
  109. System, 3(2), 179-187, 1991.
  110. GM(1,1) model (Ⅰ). Systems Engineer-Theory & Practice, 20(4), 98-103, 2000. (in
  111. Chinese)
  112. Tien, T. L., Chen, S.P, Residual correction method of Fourier series to GM(1,1) Model. The
  113. 93-101, 1996. (in Chinese)
  114. Zhou, C. Y., Li, D. F., Liu, Z. X., Grey predicting the soft ground settlement via GM(1,1).
  115. The Journal of Grey System, 11(4), 397-402, 1999.
  116. electricity demand. Energy, 31(14), 2839-2847, 2006.
Times Cited
  1. 容萍(2011)。灰預測應用於台灣蔬果產地價格之分析─以愛文芒果為例。成功大學企業管理學系碩士在職專班學位論文。2011。1-52。 
  2. 張黃傑(2012)。平面銑削之灰色即時可調式學習表面粗糙度預測系統開發。中原大學工業與系統工程研究所學位論文。2012。1-82。
  3. 江謝宇烜(2016)。建構以銑削尺寸為依據之灰色刀具壽命預警系統。中原大學工業與系統工程研究所學位論文。2016。1-65。