Consumption forecasting of missile spare parts based on wavelet transform and revised GM-ARMA model

Aiming at the characteristic of missile spare parts consumption presenting "small sample and non-stationary", and to overcome the deficiency that traditional forecasting method builds models with abundant date sample,the combined forecast method based on wavelet transform and revised GM-ARMA was proposed to carry through consumption forecasting of missile spare parts. In the process of building a hybrid model by combining wavelet decomposition with other forecasting model,first the new parameters of wavelet functions and decomposition levels were introduced, and all the parameters as a whole for the purpose of building an optimal hybrid model were quantitatively estimated. Then the high frequency signals were forecasted with ARMA model optimized by damping least-squares method, and the low frequency was forecasted with improved GM(1,1) model based on background value optimization and transformation. Experimental results show that the combined forecasting method significantly reduces forecast errors and is valid, feasible and useful in terms of forecasting missile spare parts consumption.