Table of Contents Author Guidelines Submit a Manuscript
Modelling and Simulation in Engineering
Volume 2015, Article ID 126738, 10 pages
http://dx.doi.org/10.1155/2015/126738
Research Article

A New Approach to Improve Accuracy of Grey Model GMC in Time Series Prediction

1Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
2Department of Mathematics, Faculty of Science and Technology, Kamphaeng Phet Rajabhat University, Kamphaeng Phet 62000, Thailand

Received 2 September 2015; Accepted 17 November 2015

Academic Editor: Min-Chie Chiu

Copyright © 2015 Sompop Moonchai and Wanwisa Rakpuang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. L. Deng, “Control problem of grey systems,” Systems and Control Letters, vol. 1, no. 5, pp. 288–294, 1982. View at Publisher · View at Google Scholar · View at Scopus
  2. T. Leephakpreeda, “Grey prediction on indoor comfort temperature for HVAC systems,” Expert Systems with Applications, vol. 34, no. 4, pp. 2284–2289, 2008. View at Publisher · View at Google Scholar · View at Scopus
  3. Y. M. Atwa and E. F. El-Saadany, “Annual wind speed estimation utilizing constrained grey predictor,” IEEE Transactions on Energy Conversion, vol. 24, no. 2, pp. 548–550, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. L. Youxin, W. Xiao, L. Min, and C. Anhui, “Grey dynamic model GM(1,N) for the relationship of cost and variability,” Kybernetes, vol. 38, no. 3-4, pp. 435–440, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. Z. He, Q. Wang, Y. Shen, and Y. Wang, “Discrete multivariate gray model based boundary extension for bi-dimensional empirical mode decomposition,” Signal Processing, vol. 93, no. 1, pp. 124–138, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. Z.-X. Wang and L.-L. Pei, “An optimized Grey dynamic model for forecasting the output of high-tech industry in China,” Mathematical Problems in Engineering, vol. 2014, Article ID 586284, 7 pages, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  7. L. Liu, Q. Wang, M. Liu, and L. Li, “An intelligence optimized rolling grey forecasting model fitting to small economic dataset,” Abstract and Applied Analysis, vol. 2014, Article ID 641514, 10 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. Z. X. Wang, “Nonlinear grey prediction model with convolution integral NGMC (1, n) and its application to the forecasting of China's industrial SO2 emissions,” Journal of Applied Mathematics, vol. 2014, Article ID 580161, 9 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. S. Liu and Y. Lin, Grey Information—Theory, and Practical Applications, Springer, London, UK, 2006.
  10. T.-L. Tien, “The indirect measurement of tensile strength of material by the grey prediction model GMC(1, n),” Measurement Science and Technology, vol. 16, no. 6, pp. 1322–1328, 2005. View at Publisher · View at Google Scholar · View at Scopus
  11. T. Y. Pai, R. J. Chiou, and H. H. Wen, “Evaluating impact level of different factors in environmental impact assessment for incinerator plants using GM (1, N) model,” Waste Management, vol. 28, no. 10, pp. 1915–1922, 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. L.-C. Hsu, “Forecasting the output of integrated circuit industry using genetic algorithm based multivariable grey optimization models,” Expert Systems with Applications, vol. 36, no. 4, pp. 7898–7903, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. L.-C. Hsu and C.-H. Wang, “Forecasting integrated circuit output using multivariate grey model and grey relational analysis,” Expert Systems with Applications, vol. 36, no. 2, pp. 1403–1409, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. T.-L. Tien, “A research on the grey prediction model GM(1,n),” Applied Mathematics and Computation, vol. 218, no. 9, pp. 4903–4916, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  15. W.-Y. Wu and S.-P. Chen, “A prediction method using the grey model GMC(1, n) combined with the grey relational analysis: a case study on internet access population forecast,” Applied Mathematics and Computation, vol. 169, no. 1, pp. 198–217, 2005. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  16. T.-L. Tien, “The deterministic grey dynamic model with convolution integral DGDMC(1, n),” Applied Mathematical Modelling, vol. 33, no. 8, pp. 3498–3510, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  17. H. Guo, X. Xiao, and J. Forrest, “A research on a comprehensive adaptive grey prediction model CAGM(1, N),” Applied Mathematics and Computation, vol. 225, pp. 216–227, 2013. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Guo, J.-H. Lan, and J.-J. Li, “Traffic flow data recovery algorithm based on gray residual GM(1,N) model,” Journal of Transportation Systems Engineering and Information Technology, vol. 12, no. 1, pp. 42–47, 2012. View at Google Scholar · View at Scopus
  19. J. Ren, S. Gao, S. Tan, and L. Dong, “Prediction of the yield of biohydrogen under scanty data conditions based on GM(1,N),” International Journal of Hydrogen Energy, vol. 38, no. 30, pp. 13198–13203, 2013. View at Publisher · View at Google Scholar
  20. R. Guo, “Modeling imperfectly repaired system data via grey differential equations with unequal-gapped times,” Reliability Engineering & System Safety, vol. 92, no. 3, pp. 378–391, 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. S. P. Chen and C. M. Shih, “Diffusion forecasting of innovative products using an improved grey model,” The Journal of Grey System, vol. 10, no. 1, pp. 23–32, 2007. View at Google Scholar
  22. K. Peng and X. Xiao, “GMC(1, N) power model based on cluster analysis and its application in predicting ultimate bearing capacity of single pile,” in Proceedings of the 2nd International Conference on Information Engineering and Computer Science (ICIECS '10), pp. 1–4, Wuhan, China, December 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. N.-M. Xie and S.-F. Liu, “Discrete grey forecasting model and its optimization,” Applied Mathematical Modelling, vol. 33, no. 2, pp. 1173–1186, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  24. L. Wu, S. Liu, L. Yao, and S. Yan, “The effect of sample size on the grey system model,” Applied Mathematical Modelling, vol. 37, no. 9, pp. 6577–6583, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  25. N. R. Farnum and L. W. Stanton, Quantitative Forecasting Method, PWS-Kent, Boston, Mass, USA, 1989.
  26. Y. Q. Zhang, Y. Ai, K. J. Dai, and G. D. Zhang, “Grey model via polynomial for image denoising,” Journal of Grey System, vol. 22, no. 2, pp. 117–128, 2010. View at Google Scholar · View at Scopus
  27. L.-C. Hsu, “Using improved grey forecasting models to forecast the output of opto-electronics industry,” Expert Systems with Applications, vol. 38, no. 11, pp. 13879–13885, 2011. View at Publisher · View at Google Scholar · View at Scopus
  28. S.-L. Ou, “Forecasting agricultural output with an improved grey forecasting model based on the genetic algorithm,” Computers and Electronics in Agriculture, vol. 85, pp. 33–39, 2012. View at Publisher · View at Google Scholar · View at Scopus
  29. C. Lewis, Industrial and Business Forecasting Methods, Butterworth Scientific, London, UK, 1982.
  30. S. A. DeLurgio, Forecasting Principles and Applications, Irwin/McGraw-Hill, New York, NY, USA, 1998.
  31. V. Bevilacqua, F. Intini, and S. Kühtz, “A model of artificial neural network for the analysis of climate change,” in Proceedings of the 28th International Symposium on Forecasting, pp. 22–25, Nice, France, June 2008.
  32. K. Kandananond, “Forecasting electricity demand in Thailand with an artificial neural network approach,” Energies, vol. 4, no. 8, pp. 1246–1257, 2011. View at Publisher · View at Google Scholar · View at Scopus