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SYSTEM AND METHOD FOR FORECASTING HIGH-SELLERS USING MULTIVARIATE BAYESIAN TIME SERIES

机译:多元贝叶斯时间序列预测高价的系统和方法

摘要

A system and method for grouping units for forecasting purposes is presented. A sales forecast for a set of stock keeping units (SKUs) is desired. The SKUs are separated into clusters based on the similarity of the SKUs. Then a set of Bayesian multivariate dynamic linear models is chosen to be ‘21retfgvd5xzrtfgvbyhsdcused to calculate a sales forecast for each of the clusters of SKUs. The accuracy of each dynamic linear model is determined in a training procedure and a set of weights for each dynamic linear model is calculated. Thereafter, the weights can be used with the dynamic linear models to create a weighted average forecast model. The training procedure can be run periodically to maintain the accuracy of the weights. Each procedure can operate on a sliding window of data. Other embodiments are also disclosed herein.
机译:提出了一种将单元分组以进行预测的系统和方法。需要一组库存单位(SKU)的销售预测。根据SKU的相似性,将SKU分为多个群集。然后选择一组贝叶斯多元动态线性模型作为'21retfgvd5xzrtfgvbyhsdcused,以计算每个SKU集群的销售预测。在训练过程中确定每个动态线性模型的准确性,并为每个动态线性模型计算一组权重。此后,权重可与动态线性模型一起使用以创建加权平均预测模型。训练程序可以定期运行以保持重量的准确性。每个过程都可以在数据的滑动窗口上进行。本文还公开了其他实施例。

著录项

  • 公开/公告号US2016260052A1

    专利类型

  • 公开/公告日2016-09-08

    原文格式PDF

  • 申请/专利权人 WAL-MART STORES INC.;

    申请/专利号US201514641075

  • 发明设计人 SHUBHANKAR RAY;ABHAY JHA;

    申请日2015-03-06

  • 分类号G06Q10/08;G06Q30/02;

  • 国家 US

  • 入库时间 2022-08-21 14:33:25

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