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基于Stacking模型融合的店铺销量预测

来源:用户上传      作者:王鹏 曹丽惠 阮冬茹

  摘 要:为了实现企业产品销量预估,提高生产供应的准确性与效率,提出了基于Stacking模型的融合算法进行销量预测。算法设计了两层堆叠的模型结构,初级学习器采用随机森林、支持向量回归、差分整合移动平均自回归、轻量级梯度提升机器和门控循环单元5种单模型,将分类与回归树作为次级学习器构成Stacking融合模型,并对数据进行了预测。预测结果显示,使用Stacking模型融合后得到了较好的预测结果,比单模型中效果最好的模型的均方根误差更小,平均绝对误差更小,决定系数值更大,表明Stacking融合模型的预测准确率更高。所设计模型可用于对企业店铺的产品销量进行预测,帮助企业更好地安排生产、营销活动,为减少库存、缩短生产销售周期提供数据支持,对企业生产决策有一定的参考价值。
  关键词:计算机决策支持系统;销量预测;Stacking;模型融合;初级学习器;次级学习器
  中图分类号:TP311.13 文献标识码:A
   DOI: 10.7535/hbgykj.2022yx03004
  Store sales forecast based on Stacking model fusion
  WANG Peng1,CAO Lihui2,RUAN Dongru1
  (1.School of Information Science and Engineering,Hebei University of Science and Technology,Shijiazhuang,Hebei 050018,China;2.The 54th Research Institute of CETC,Shijiazhuang,Hebei 050081,China)
  Abstract:In order to realize the sales volume prediction in modern enterprises and achieve the accuracy and efficiency of production and supply,a fusion algorithm based on Stacking model was proposed for sales volume prediction.The algorithm designed a two-layer stacked model structure.The primary learner adopted five single models: RF(random forest),SVR(support vector regression),ARIMA(autoregressive integrated moving average),LGBM(light gradient boosting machine),GRU (gated recurrent unit),CART (classification and regression tree) was used as secondary learners to form a Stacking fusion model,and the data were predicted.The prediction results show that there are better prediction results in the relevant data after using the Stacking model fusion.Compared with the model with the best effect in the single model,the RMSE (root mean square error) is smaller,the MAE (mean absolute error) is smaller,and the R2 (coefficient of determination) value is larger,indicating that the prediction accuracy of the Stacking fusion model is higher.This model can be used to predict the product sales and other relevant data of enterprise stores,help enterprises better arrange production and marketing,provide reference data for reducing inventory and shortening production and sales cycle,and have a certain reference value for enterprise production decision-making.
  Keywords:
  computer decision support system;sales forecast;Stacking;model fusion;primary learner;secondary learner
  N量预测是根据企业过去的经营状况和相关资料,对其在一定时期内的销售数量进行预计和推测。现如今,食品类零售企业大多采取以销定产的策略,由市场销售人员根据订单和销售经验预测未来一段时间的销量,分销点逐级汇报汇总编制下一周期的经营计划,生产部门安排生产进度。这种传统的生产计划方式随着市场环境变化、影响产品销售因素增多等暴露出明显的不足,时常发生因个人主观判断失误造成库存积压导致亏损等情况,而在店铺数量增加,生产成本和物流成本也增多的情况下,亏损程度更是成倍增长。同时如果店铺订货过于保守,出现缺货、少货的现象,也会大大降低客户体验度,损害品牌形象。比如某肉类零售企业所面临的以销定产问题,肉类食品保质期短,全程冷链物流,运输成本高,若临期低价销售,则会影响市场售价平衡,若过期返厂统一销毁,则会增加二次运输成本,因此准确预测销售数量成为这类企业的重要工作内容。

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