An MC2 linear programming approach to combined forecasting

Authors:
Zhou Zongfang;Y. Shi;X. Hao
Affiliations:
Department of Basic Science Chongqing University of Posts and Telecommunications Chongqing 630065, P.R. China;Department of Information System and Quantitative Analysis University of Nebraska at Omaha Omaha, NE 68182, U.S.A.;Department of Information System and Quantitative Analysis University of Nebraska at Omaha Omaha, NE 68182, U.S.A.
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
1999

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Abstract

Combined forecasting is a well-known forecasting technique of handling multiple forecasting methods. However, the previous combined forecasting approaches lack of incorporating the various possible opinions from several experts on an given forecasting problem. This paper proposes an MC^2 linear programming approach to determining weighted coefficients of combined forecasting that involves multiple experts. The numerical example of the paper shows that the proposed approach likely outperforms the current techniques of combined forecasting in dealing with the case of multiple experts.