基于影响因素的SVR备件需求预测研究

Research on demand-forecasting for spare parts of Support Vector Regression(SVR) based on impact factors

文章介绍了支持向量机学习算法,说明其特点,并引出基于影响因素的支持向量回归的备件需求预测方法,用某型备件的历史需求数据例证此法的可行性与精确度。

This paper introduces the algorithm of machine learning of Support Vector Machines （SVM） and illuminates its characters, educing the forecasting method for spare parts demanded by Support Vector Regressin （SVR） based on impact factors. By analyzing the former-demand data of some type spare parts, we demonstrate the feasibility and precision of this method.