Abstract: In view of the disadvantage that the traditional GM(1,1) grey prediction model adopts equal weight in the background value, which results in low prediction accuracy, this paper proposes a variable weight optimization method for selecting background value. Firstly, the golden section search and parabolic interpolation method are combined to determine the background value of the improved GM (1,1) model. Then the improved background value is brought into the grey prediction algebraic recursive equation, replacing the whitening equation in the traditional GM(1,1) grey prediction model. Finally, we select the exponential sequence to simulate, and carry out a simulation experiment based on the actual statistical data of the number of teachers in a university. The results show that the improved GM (1,1) model reduces the average relative error, improves the prediction accuracy and has certain application value.

Key words: grey prediction, golden section, parabolic interpolation method, variable weight optimization