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This paper aims to study a new grey prediction approach and its solution for forecasting the main system variable whose accurate value could not be collected while the potential value set could be defined. Based on the traditional nonhomogenous discrete grey forecasting model(NDGM), the interval grey number and its algebra operations are redefined and combined with the NDGM model to construct a new interval grey number sequence prediction approach. The solving principle of the model is analyzed, the new accuracy evaluation indices, i.e. mean absolute percentage error of mean value sequence(MAPEM) and mean percent of interval sequence simulating value set covered(MPSVSC), are defined and, the procedure of the interval grey number sequence based the NDGM(IG-NDGM) is given out. Finally, a numerical case is used to test the modelling accuracy of the proposed model. Results show that the proposed approach could solve the interval grey number sequence prediction problem and it is much better than the traditional DGM(1,1) model and GM(1,1) model.
This paper aims to study a new gray prediction approach and its solution for forecasting the main system variable whose accurate value could not be collected while the potential value set could be defined. Based on the traditional nonhomogenous discrete gray forecasting model (NDGM), the interval gray number and its algebra operations are redefined and combined with the NDGM model to construct a new interval gray number sequence prediction approach. The solving principle of the model is analyzed, the new accuracy evaluation indices, ie, mean absolute percentage error of mean value sequence MAPEM) and mean percent of interval sequence simulating value set covered (MPSVSC), are defined and the procedure of the interval gray number sequence based on NDGM (IG-NDGM) is given out. Finally, a numerical case is used to test the moderation accuracy of the proposed model. Results show that the proposed approach could solve the interval gray number sequence prediction problem and it is much better than the traditional DGM (1,1) model and GM (1,1) model.