摘要
为了更好地了解生产物料需求量变化情况,可以采用数学模型进行预测,解决生产物料需求量变化非线性回归问题,提高预测效果。基于此,本文探讨生产物料需求量数据收集与预处理,分析常见的生产物料需求量预测模型,包括ARIMA模型、SVR模型、BP神经网络模型,并采用案例分析方法,讨论模型在生产物料需求量预测中的实际应用,以期为生产物料需求量预测工作提供参考。
In order to better understand the changes in production material demand,mathematical models can be used for prediction to solve the problem of nonlinear regression of production material demand changes and improve the prediction effect.Based on this,this article explores the collection and preprocessing of production material demand data,analyzed several production material demand forecasting models,including ARIMA model,SVR model,BP neural network model,using case analysis method,discussed the practical application of several models in predicting production material demand,in order to provide reference for the prediction of production material demand.
作者
贾广跃
吴发杰
JIA Guangyue;WU Fajie(CRRC Qingdao Sifang Co.,Ltd.,Qingdao Shandong 266111)
出处
《中国科技纵横》
2025年第18期48-50,共3页
China Science & Technology Overview
关键词
生产物料
需求量预测
预测模型
production materials
demand forecasting
prediction model
