Properties of Weighted Geometric Means Combination Forecasting Model Based on Absolute of Grey Incidence

Article Preview

Abstract:

weighted geometric means combination forecasting is a kind of nonlinear combination forecasting model. Based on absolute of grey incidence, a weighted geometric means combination forecasting model is proposed. Superior combination forecasting, dominant forecasting method and redundant degree are put forward. Under certain conditions the sufficient condition of existence of non-inferior combination and superior combination forecasting are discussed, redundant information is pointed out in a judging theorem.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 490-495)

Pages:

442-446

Citation:

Online since:

March 2012

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Bates J M, Granger C W J. Combination of forecasts[J]. Operations Research Quarterly, 1969, 20(4): 451-468.

Google Scholar

[2] Yingming Wang, Guowei Fu. Study on the Methods of Combing Forecasts Based On Different Kinds of Error Criterion and Norms[J]. Control and Decision. 1994, 9(1): 20-28.

Google Scholar

[3] Chuanshi Zhou, Guomin Luo. Weight Geometric Average Combination Forecast Moded and Its Applications[J] APPLICATION OF STATISTICS AND MANAGEMENT, 1995, 14(2): 17-19.

Google Scholar

[4] Andong Lin. The Combining Forecasting Model on the Condition of the Minimal Sum of the Error Tolerances' Absolute Values and Its Application[J]. Journal of Shanghai Maritime University, 2000, 21(3): 95-101.

Google Scholar

[5] Huayou Chen, Chunlin Liu. Properties of weighted geometric means combination forecasting model based on L1 norm[J] Journal of Southeast University (Natural Science Edition), 2004, 34(4): 535-540.

Google Scholar

[6] Linghua Cheng, Huayou Chen. Properties of weighted means combination forecasting method based on Theil coefficient[J]Opperations Research and Management Science, 2007, 16(2): 78-83.

Google Scholar

[8] Yingming Wang. Research on the Methods of Combining Forecasts Based on Correlativity [J]. Rorecasting, 2002, 21(4): 448-454.

Google Scholar

[9] Huayou Cen, Jiabao Zhao, Chunlin LIU. Properties of combination forecasting model based on degree of grey incidence[J]. Journal of Southeast University , 2004, 34(1): 130-134.

Google Scholar

[10] Huayou Cen. Research on properties of superior combined forecasting models based on correlation coefficients [J]. Journal of Systems Engineering, 2006, 21(4): 353-360.

Google Scholar

[11] Linghua Cheng, Huayou Cen. Efficiency of Weighted Geometric Means Combination Forecasting Model Based on Degree of Logarithm Grey Incidence[J] Operations Research and Management Science, 2007, 16(6): 69-73.

DOI: 10.1109/gsis.2007.4443359

Google Scholar

[12] Shang Gao, Shaobiao Zhang, MEI Liag. Linear combination forecast based on relative error criterion [J]. Systems Engineering and Electronics, 2008, 30(3): 481-484.

Google Scholar

[13] Shaoliang Tang, Nan Li, Zaiwu Gong. Research on properties of combination forecasting model based on absolute of grey incidence[J]. Systems Engineering and Electronics, 2008, 30(1): 89-92.

Google Scholar

[14] Lihong Sun, Jihong Shen. Properties of weighted geometric means combination forecasting model based on correlation coefficients[J] Systems Engineering-Theory & Practice, 2009, 9(13).

Google Scholar

[15] Sifeng Liu, Yaoguo Dang , Zhigeng Fang. The grey system theory and Application[M]. Science Press, (2004).

Google Scholar