递归特征消除法在商品需求预测的应用

Application of the Recursive Feature Elimination Method in Commodity Demand Prediction

需求预测作为企业供应链的第一道防线,有着至关重要的作用.需求预测因受多种因素的影响,准确率长期不佳.文中借助Python的可视化库进行数据可视化操作,通过特征相关性分析增强数据的可读性.采用XGBoost、LightGBM和随机森林等机器学习模型,寻找对需求量影响较大的特征,运用递归特征消除法对特征进行排序得分,以获得更准确的需求预测结果 .提取、添加特征并分组合并,以提高预测的精确性.基于递归特征消除法并利用数据可视化提升数据提取准备度,在具体应用实例能提高准确率和缩短运行时间.

Demand forecasting plays a crucial role as the first line of defense in the enterprise supply chain.The accuracy of demand forecasting has been consistently poor due to various factors.In the article,Python’s visualization library is used for data visualization operations,and feature correlation analysis is used to enhance the readability of the data.Using machine learning models such as XGBoost,LightGBM,and Random Forest,we search for features that have a significant impact on demand,and use recursive feature elimination to rank and score the features to obtain more accurate demand prediction results.Extract,add features,group and merge them to improve the accuracy of prediction.Based on recursive feature elimination method and utilizing data visualization to improve data extraction readiness,it can improve accuracy and shorten running time in specific application examples.