Combination forecasting method for development cost of aircraft

To address the problems of scarce sample data, complex influence factors and low forecasting quality of a single prediction method in predicting the aircraft development costs, a combination forecasting method is adopted. Based on the sample data, the radial basis function (RBF) artificial neural network, Gram-Schmidt regression and partial least squares regression (PLSR) are combined to construct the combination forecasting model, which is also compared with the single prediction method. The results show that the combination forecasting method has satisfactory and stable prediction accuracy, and it can reduce the quality risk of the single prediction method, so it is a reliable and effective method for aircraft development costs prediction.