Predictor Design of a Novel Grey Model PGM21 Using Pseudo Second-Order Information

Abstract

A novel grey model, called pseudo-GM(2,1) or PGM21 for simplicity, is developed to improve the prediction accuracy for a non-monotone sequence by modifying the GM(1,1) model with a remedial term. Since the remedial term consists of two development coefficients, it can be treated as a kind of second-order information, called the pseudo second-order information in this paper. With the remedial term, the PGM21 only costs a little higher computation time than the original GM(1,1) model; however, its prediction accuracy is highly increased. The important features of the PGM21 are demonstrated by numeric simulation results.