基于LS-SVM的装备需求时间序列预测

Time Series Prediction of Military Equipment based on Least Square Support Vector Machine

支持向量机(SVM)是一种基于结构风险最小化原理的学习技术,是一种新的具有很好泛化性能的数据挖掘工具。文中在最小二乘支持向量机(LS-SVM)回归算法的基础上,引入了混沌理论时间序列理论的相空间和嵌入维数概念,建立了LS-SVM时间序列预测模型,并应用于武器装备需求预测,预测结果证实了该模型和方法的有效性。

Support vector machine(SVM)is a learniug technology based on the structure risk minimization principle,and is also a new kind of data mining tool with good generalization.In this article,a time series forecasting model based on the least square support vector machine(LS-SVM)regression theory is given by induced the concept of phase space and embedding dimension from chaotic time series theory.Finally the application in the military equipment time series pre- diction of the model will be shown to validate the prediction precision of the method.