摘要
需求预测是企业生产运营决策的基础,预测精度影响着产品的安全库存量,关系到企业利润和市场竞争力.建立了一类产品(包含多个品牌)基于支持向量机(support vector machine,SVM)的需求预测模型.在该预测模型中考虑了诸如季节性和促销等不确定性因子对产品最终需求的影响.模型的训练数据和测评数据采用的是由该类产品需求函数生成的数据.测试阶段的评价则是通过与其他统计模型(回归预测方法(REG)、双因素指数平滑法(DES)、Winter模型预测方法(WIN))和径向基神经网络模型(radial basis function neural network,RBFNN)的对比来实现的.实验结果表明,基于SVM的需求预测模型预测精度明显优于其他模型,有效地降低了产品安全库存量,提高了企业利润,为解决这类产品需求预测问题提供了一个有力的工具.
Demand forecast is the basis of business operation for a company. Forecast accuracy has a great effect on safety inventory, profit and competitiveness. In this paper, support vector machine （SVM） is used to forecast the demand of a product category including multiple brands. Various factors that affect the product demand such as seasonal and promotional factors are taken into consideration. Data used in model training and assessment are generated from the demand function of this product. Different forecast models such as regression model, double exponent smooth model, Winter model and radial basis function neural network model are used for comparison and evaluation. The results show that accuracy of SVM is superior to other models, which can significantly reduce the inventory level. Therefore SVM is shown to be an effective model for demand forecast.
出处
《上海大学学报（自然科学版）》
CAS
CSCD
北大核心
2009年第1期71-76,共6页
Journal of Shanghai University:Natural Science Edition
基金
国家自然科学基金资助项目(70502020)
上海市教委基金资助项目(05AS98)
关键词
需求预测
支持向量机
统计预测方法
神经网络
demand forecast
support vector machine （SVM）
statistical forecast method
neural network
