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1. 发明申请

US20160260111A1 SYSTEM AND METHOD FOR GROUPING TIME SERIES DATA FOR FORECASTING PURPOSES 审中-公开
标题翻译：用于分类用于预测用途的时间序列数据的系统和方法
公开(公告)号：US20160260111A1
公开(公告)日：2016-09-08
申请号：US14638694
申请日：2015-03-04
申请人： Wal-Mart Stores, Inc.
发明人： Shubhankar Ray , Abhay Jha
IPC分类号： G06Q30/02 , G06Q10/08 , G06K9/62
CPC分类号： G06Q30/0202 , G06K9/6218 , G06Q10/087
摘要： A system and method for grouping units for forecasting purposes is presented. A plurality of stock keeping units (SKUs) is presented to an embodiment. Initial medoids are chosen based on a vertex within a set of vertices, each of which represent a SKU. Then, each vertex within the set of vertices is associated with its closest medoid to form initial clusters. There can be a cap on the number of vertices in each cluster. Thereafter, an iterative algorithm is performed wherein a probability is assigned to each vertex. One or more vertices are randomly chosen, with the weights of the vertices weighting the random choice. The chosen one or more vertices are moved to another cluster. The algorithm is performed until no further improvements result from moving one or more vertices to another cluster. Other embodiments are also disclosed herein.
摘要翻译：介绍了用于将单位分组以进行预测的系统和方法。多个库存单元（SKU）被呈现给实施例。基于一组顶点内的顶点来选择初始的类型，每个顶点代表SKU。然后，顶点集合中的每个顶点与其最近的中间体相关联，以形成初始聚类。每个群集中的顶点数可以有一个上限。此后，执行其中将概率分配给每个顶点的迭代算法。一个或多个顶点被随机选择，顶点的权重加权随机选择。所选的一个或多个顶点被移动到另一个集群。执行算法，直到将一个或多个顶点移动到另一个集群为止不再有改进。本文还公开了其它实施例。

2. 发明申请

US20160260110A1 SYSTEM AND METHOD FOR PREDICTING THE SALES BEHAVIOR OF A NEW ITEM 审中-公开
标题翻译：用于预测新项目的销售行为的系统和方法
公开(公告)号：US20160260110A1
公开(公告)日：2016-09-08
申请号：US14638637
申请日：2015-03-04
申请人： Wal-Mart Stores, Inc.
发明人： Shubhankar Ray , Abhay Jha
IPC分类号： G06Q30/02
CPC分类号： G06Q30/0202
摘要： A system and method for forecasting the sales of a new item, i.e., one with no historical sales data, is presented. Two matrices are presented, a feature matrix and a sales matrix. The matrices are divided into training matrices and prediction matrices. The training matrices are decomposed, then regression analysis is performed to determine the weight of various columns of the training feature matrix. Thereafter, the weights could be used on the training prediction matrix to predict sales. The sales predictions can be used to order SKUs for a retailer or distributor. Other embodiments are also disclosed herein.
摘要翻译：呈现用于预测新项目的销售的系统和方法，即，没有历史销售数据的销售。呈现两个矩阵，一个特征矩阵和一个销售矩阵。矩阵分为训练矩阵和预测矩阵。训练矩阵被分解，然后进行回归分析以确定训练特征矩阵的各列的权重。此后，可以在训练预测矩阵中使用权重来预测销售。销售预测可用于为零售商或分销商订购SKU。本文还公开了其它实施例。

3. 发明授权

US10521809B2 System and method for grouping time series data for forecasting purposes 有权
公开(公告)号：US10521809B2
公开(公告)日：2019-12-31
申请号：US14638694
申请日：2015-03-04
申请人： Wal-Mart Stores, Inc.
发明人： Shubhankar Ray , Abhay Jha
IPC分类号： G06Q10/04 , G06Q30/02 , G06K9/62 , G06Q10/08
摘要： A system and method for grouping units for forecasting purposes is presented. A plurality of stock keeping units (SKUs) is presented to an embodiment. Initial medoids are chosen based on a vertex within a set of vertices, each of which represent a SKU. Then, each vertex within the set of vertices is associated with its closest medoid to form initial clusters. There can be a cap on the number of vertices in each cluster. Thereafter, an iterative algorithm is performed wherein a probability is assigned to each vertex. One or more vertices are randomly chosen, with the weights of the vertices weighting the random choice. The chosen one or more vertices are moved to another cluster. The algorithm is performed until no further improvements result from moving one or more vertices to another cluster. Other embodiments are also disclosed herein.

4. 发明申请

US20160239776A1 SYSTEM AND METHOD FOR ITEM-LEVEL DEMAND FORECASTS USING LINEAR MIXED-EFFECTS MODELS 审中-公开
标题翻译：使用线性混合效应模型的项目级需求预测的系统和方法
公开(公告)号：US20160239776A1
公开(公告)日：2016-08-18
申请号：US14622252
申请日：2015-02-13
申请人： Wal-Mart Stores, Inc.
发明人： Huijun Feng , Shubhankar Ray , Abhay Jha
IPC分类号： G06Q10/06 , G06Q10/08 , G06Q30/02
CPC分类号： G06Q10/087 , G06Q10/06315 , G06Q30/0201 , G06Q30/0202 , Y04S10/54
摘要： A system and method for forecasting sales is presented. A method might begin by receiving a request to produce a demand forecast for a stock keeping unit (SKU). Then, the SKU is placed in one or more clusters. A cluster seasonality profile is calculated for each of the one or more clusters. An item seasonality profile is calculated for the SKU. Then the demand forecast for the SKU is generated. The demand forecast is adjusted using the cluster seasonality profile for each of the one or more clusters and the item seasonality profile for the SKU. Then inventory can be ordered based on the adjusted demand forecast. Other embodiments are also disclosed herein.
摘要翻译：介绍了销售预测的系统和方法。一种方法可以从接收到为库存单位（SKU）生成需求预测的请求开始。然后，将SKU置于一个或多个簇中。针对一个或多个群集中的每个群集计算群集季节特征。为SKU计算项目季节性概况。然后生成SKU的需求预测。对于一个或多个集群中的每个集群和SKU的项目季节性配置文件，使用集群季节性配置文件来调整需求预测。然后可以根据调整后的需求预测订购库存。本文还公开了其它实施例。

5. 发明授权

US11093954B2 System and method for predicting the sales behavior of a new item 有权
公开(公告)号：US11093954B2
公开(公告)日：2021-08-17
申请号：US14638637
申请日：2015-03-04
申请人： Wal-Mart Stores, Inc.
发明人： Shubhankar Ray , Abhay Jha
IPC分类号： G06Q30/02 , G06Q10/06 , G06Q10/08
摘要： A system and method for forecasting the sales of a new item, i.e., one with no historical sales data, is presented. Two matrices are presented, a feature matrix and a sales matrix. The matrices are divided into training matrices and prediction matrices. The training matrices are decomposed, then regression analysis is performed to determine the weight of various columns of the training feature matrix. Thereafter, the weights could be used on the training prediction matrix to predict sales. The sales predictions can be used to order SKUs for a retailer or distributor. Other embodiments are also disclosed herein.

6. 发明授权

US10373105B2 System and method for item-level demand forecasts using linear mixed-effects models 有权
公开(公告)号：US10373105B2
公开(公告)日：2019-08-06
申请号：US14622252
申请日：2015-02-13
申请人： Wal-Mart Stores, Inc.
发明人： Huijun Feng , Shubhankar Ray , Abhay Jha
IPC分类号： G06Q10/00 , G06Q10/08 , G06Q10/06 , G06Q30/02
摘要： A system and method for forecasting sales is presented. A method might begin by receiving a request to produce a demand forecast for a stock keeping unit (SKU). Then, the SKU is placed in one or more clusters. A cluster seasonality profile is calculated for each of the one or more clusters. An item seasonality profile is calculated for the SKU. Then the demand forecast for the SKU is generated. The demand forecast is adjusted using the cluster seasonality profile for each of the one or more clusters and the item seasonality profile for the SKU. Then inventory can be ordered based on the adjusted demand forecast. Other embodiments are also disclosed herein.

7. 发明授权

US10453026B2 System and method for forecasting high-sellers using multivariate bayesian time series 有权
公开(公告)号：US10453026B2
公开(公告)日：2019-10-22
申请号：US14641075
申请日：2015-03-06
申请人： Wal-Mart Stores, Inc.
发明人： Shubhankar Ray , Abhay Jha
IPC分类号： G06Q10/00 , G06Q30/00 , G06Q10/08 , G06Q30/02
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 used 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.

8. 发明申请

US20160328724A1 SYSTEM AND METHOD FOR FORECASTING WITH SPARSE TIME PANEL SERIES USING DYNAMIC LINEAR MODELS 审中-公开
标题翻译：使用动态线性模型预测时间面板系统的系统和方法
公开(公告)号：US20160328724A1
公开(公告)日：2016-11-10
申请号：US14705456
申请日：2015-05-06
申请人： Wal-Mart Stores, Inc.
发明人： Shubhankar Ray , Abhay Jha
IPC分类号： G06Q30/02 , G06Q10/08
CPC分类号： G06Q30/0202 , G06Q10/087
摘要： A system and method for forecasting sales is presented. A set of stock keeping units (SKUs) is received, then placed into a plurality of clusters of SKUs. A set of dynamic linear models and associated parameters are chosen to create a forecast for each cluster in the plurality of clusters of SKUs. A sequential learning algorithm is used to create a weighting of each dynamic linear model in the set of dynamic linear models. The weighting of each dynamic linear model is updated using a particle learning algorithm. The particle learning algorithm comprises performing a resampling the set of dynamic linear models using a set of weights, propagating a set of state vectors through the set of dynamic linear models based on the resampling, and performing a sampling to determine parameters for the set of dynamic linear models. Then a sales forecast is generated and inventory can be ordered. Other embodiments are also disclosed herein.
摘要翻译：介绍了销售预测的系统和方法。收到一组库存单位（SKU），然后将其放入多个SKU集群。选择一组动态线性模型和相关联的参数来创建SKU的多个簇中的每个簇的预测。使用顺序学习算法来创建动态线性模型集合中每个动态线性模型的加权。使用粒子学习算法更新每个动态线性模型的加权。粒子学习算法包括使用一组权重来对该组动态线性模型进行重采样，基于重采样在一组动态线性模型中传播一组状态向量，并且执行采样以确定动态集合的参数线性模型。然后生成销售预测，并可订购库存。本文还公开了其它实施例。

9. 发明申请

US20160260052A1 SYSTEM AND METHOD FOR FORECASTING HIGH-SELLERS USING MULTIVARIATE BAYESIAN TIME SERIES 审中-公开
标题翻译：使用多元贝叶斯时间序列预测高卖方的系统和方法
公开(公告)号：US20160260052A1
公开(公告)日：2016-09-08
申请号：US14641075
申请日：2015-03-06
申请人： WAL-MART STORES, INC.
发明人： Shubhankar Ray , Abhay Jha
IPC分类号： G06Q10/08 , G06Q30/02
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.
摘要翻译：介绍了用于将单位分组以进行预测的系统和方法。需要一套库存单位（SKU）的销售预测。基于SKU的相似性，SKU被分成簇。然后选择一组贝叶斯多变量动态线性模型为“21retfgvd5xzrtfgvbyhsd”用于计算每个SKU集群的销售预测。每个动态线性模型的精度在训练过程中确定，并计算每个动态线性模型的一组权重。此后，权重可以与动态线性模型一起使用以创建加权平均预测模型。培训程序可以定期运行，以保持权重的准确性。每个过程都可以在数据的滑动窗口上操作。本文还公开了其它实施例。

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