Application of Grey Least Square Support Vector Machine in New Equipment Materiel Demand Prediction

Due to the short investment time of the new equipment, the materiel consumption and maintenance data is not much. As a result, its demand prediction belongs to the prediction of small sample data. Since general demand prediction methods are difficult to predict the materiel demand of new equipment, an applicable and efficient prediction method should be explored to solve the problem. Therefore, combining grey prediction theory and least square support vector machine and operating accumulative generation on the original data sequence to extract its deep law characteristic, the new equipment materiel demand prediction model based on Grey Least Square Support Vector Machine (GLSSVM) was established, and the model's parameters was optimized by SIWPSO. Finally an example was set using Neural Network, traditional LSVSM and GLSSVM to predict the materiel demand of new equipment X to verify the accuracy and effectiveness of GLSSVM. The result shows that the prediction precision of GLSSVM is superior to the other two methods.