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JACIII Vol.12 No.3 pp. 249-253
doi: 10.20965/jaciii.2008.p0249
(2008)

Paper:

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An Approach to Improve Estimation Performance of GM(1,1) Model

Cheng-Hsiung Hiseh, Ren-Hsien Huang, and Ting-Yu Feng

Department of Computer Science and Information Engineering, Chaoyang University of Technology, 168 Jifong E. Rd., Wufong Township Taichung County, Taiwan 41349

Received:
April 22, 2007
Accepted:
September 30, 2007
Published:
May 20, 2008
Creative Commons License
Keywords:
grey system, GM(1,1) model, data fitting, forecasting, estimation
Abstract
In this paper, we present an approach to improve estimation performance of GM(1,1) model. It consists of three stages in the GM(1,1) modeling: the preprocessing 1-AGO (first-order accumulated generating operation), first-order difference equation, and 1-IAGO (first-order inverse accumulated generating operation). Note that the solution of first-order difference equation is of exponential form. Therefore, it is assumed that the data after 1-AGO is of exponential-like form. However, in many cases the 1-AGO preprocessed data may not have an exponential-like form. Consequently, an inaccurate modeling results and thus poor performance for GM(1,1) model. Based on the observation, we replace the first-order difference equation in the GM(1,1) modeling with a polynomial to relieve the requirement of exponential-like form in the 1-AGO preprocessed data. By this doing, the estimation performance of GM(1,1) model is improved. Through examples, the proposed approach is justified. As expected, the simulation results indicate that the proposed approach outperforms the conventional GM(1,1) model in the given fitting and forecasting examples.
Cite this article as:
C. Hiseh, R. Huang, and T. Feng, “An Approach to Improve Estimation Performance of GM(1,1) Model,” J. Adv. Comput. Intell. Intell. Inform., Vol.12 No.3, pp. 249-253, 2008.
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References
  1. [1] J. L. Deng, “Control Problem of Grey System,” Control Letters, No.1, pp. 288-294, 1982.
  2. [2] J. L. Deng, “Introduction to Grey System Theory,” The Journal of Grey System, Vol.1, No.1, pp. 1-24, 1989.
  3. [3] L. Zhongmo, “Primary applications of grey system theory in the study of earthquake forecasting,” Journal of Seismology, Vol.4, pp. 27-31, 1986.
  4. [4] A. W. L. Yao, S. C. Chi, and J. H. Chen, “An improved Grey-based approach for electricity demand forecasting,” Electric Power Systems Research, Vol.67, No.3, pp. 217-224, 2003.
  5. [5] D.-H. Shen and J.-C. Du, “Grey model for asphalt pavement performance prediction,” The 7th Int. IEEE Conf. on Intelligent Transportation Systems, pp. 668-672, 2004.
  6. [6] C.-H. Hsieh, “Grey Filtering and Its Application to Speech Enhancement,” IEICE Transactions on Information and Systems, Vol.E86-D, No.3, pp. 522-533, March 2003.
  7. [7] C.-H. Hsieh and T.-Y. Feng, “Grey Voice Activity Detection,” The Journal of Grey System, Vol.18, No.3, pp. 187-193, 2006.
  8. [8] Y.-P. Huang and T.-M. Yu, “Preprocessing Data Points to Improve Grey Prediction Performance,” The Journal of Grey System, Vol.8, No.2, pp. 165-185, 1996.
  9. [9] M.-F. Yeh and H.-C. Lu, “A New Modified Grey Model,” The Journal of Grey System, Vol.8, No.3, pp. 209-216, 1996.
  10. [10] C.-C. Wong, W.-C. Liang, H.-M. Feng, and D.-A. Chiang, “Grey Prediction Controller Design,” Journal of Grey System, Vol.10, No.2, pp. 123-131, 1998.
  11. [11] J. Wu and C.-R. Lauh, “A Study to Improve GM(1,1) via Heuristic Method,” The Journal of Grey System, Vol.10, No.3, pp. 183-192, 1998.
  12. [12] N.-Y. Shih, N.-J. Shih, and W.-C. Yu, “The Parameter Estimation of Time-varying GM(1,1),” Journal of Grey System, Vol.9, No.1, pp. 51-56, 2006.
  13. [13] L.-C. Hsu, “Applying the Grey Prediction Model to the Global Integrated Circuit Industry,” Technological Forecasting and Social Change, Vol.70, pp. 563-574, 2003.
  14. [14] D. G. Childers, “Speech Processing and Synthesis Toolboxes,” John Wiley and Sons, Inc., 1999.

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