A Data-Characteristic-Driven Decomposition Ensemble Forecasting Research on the Demand of Space Science Payload Components

In order to solve the problem of long product lead time, accurate demand forecasting for space science payload components is of great significance to the development of China’s space science industry. In view of the unsteady, nonlinear, and small sample characteristics of space science payload component demand, this paper proposes the EEMD-CC&CV-MPSO-SVR model to predict the future demand of space science payload components. First, this paper effectively adopts EEMD to decompose the normalized demand sequence and analyze the stationarity of each subsequence. The sequence complexity is distinguished by sample entropy, and the optimum kernel function CC-MPSO-SVR and CV-MPSO-SVR prediction models are established for high-complexity and low-complexity sequences, respectively. Finally, the prediction results of each subsequence are ensemble to form a total prediction. Experimental results shows that the model proposed in this paper performs better than single benchmark models and other hybrid models in terms of prediction performance and robustness. It can effectively predict the quantity and trend of the demand for China’s space science payload components, which provide decision-making basis for the government to formulate policies, demand-side procurement, and supply-side inventory control.