公开(公告)号：US20110004510A1

公开(公告)日：2011-01-06

申请号：US12512071

申请日：2009-07-30

申请人： Arash Bateni ,  Edward Kim

发明人： Arash Bateni ,  Edward Kim

IPC分类号： G06Q10/00 ,  G06F17/30 ,  G06N5/02 ,  G06Q50/00

CPC分类号： G06Q10/00 ,  G06N5/046 ,  G06Q30/0201

摘要： A method system for forecasting product demand using a causal methodology, based on multiple regression techniques. The methodology utilizes weather related data as a set of causal factors for retail demand forecasting. These weather related factors may include temperature, precipitation, snow, accumulated snow, or extreme weather conditions.

摘要翻译： 一种基于多元回归技术的因果方法预测产品需求的方法系统。 该方法利用天气相关数据作为零售需求预测的一组因果因素。 这些天气相关因素可能包括温度，降水，积雪，积雪或极端天气条件。

公开(公告)号：US20090125375A1

公开(公告)日：2009-05-14

申请号：US11938812

申请日：2007-11-13

申请人： Arash Bateni ,  Edward Kim ,  Harminter Atwal ,  Jean-Philippe Vorsanger

发明人： Arash Bateni ,  Edward Kim ,  Harminter Atwal ,  Jean-Philippe Vorsanger

IPC分类号： G06Q10/00 ,  G06F17/11

CPC分类号： G06Q30/02 ,  G06Q10/04 ,  G06Q30/0202 ,  G06Q30/0206

摘要： An improved method for forecasting and modeling product demand for a product during promotional periods. The forecasting methodology employs a multivariable regression model to model the causal relationship between product demand and the attributes of past promotional activities. The model is utilized to calculate the promotional uplift from the coefficients of the regression equation. The methodology utilizes a mathematical formulation that transforms regression coefficients, a combination of additive and multiplicative coefficients, into a single promotional uplift coefficient that can be used directly in promotional demand forecasting calculations.

摘要翻译： 一种在促销期间预测和建模产品需求的改进方法。 预测方法采用多变量回归模型来模拟产品需求与过去促销活动的属性之间的因果关系。 该模型用于从回归方程的系数计算促销隆起。 该方法利用将回归系数（加法和乘法系数的组合）转换成可以在促销需求预测计算中直接使用的单个促销隆起系数的数学公式。

公开(公告)号：US20090012979A1

公开(公告)日：2009-01-08

申请号：US11772343

申请日：2007-07-02

申请人： Arash Bateni ,  Edward Kim ,  Prathayana Balendran ,  Andrew Chan

发明人： Arash Bateni ,  Edward Kim ,  Prathayana Balendran ,  Andrew Chan

IPC分类号： G06F19/00

CPC分类号： G06Q30/02 ,  Y10S707/972

摘要： A partitioning system that provides a fast, simple and flexible method for partitioning a dataset. The process, executed within a computer system, retrieves product and sales data from a data store. Data items are selected and sorted by a data attribute of interest to a user and a distribution curve is determined for the selected data and data attribute. The total length of the distribution curve is calculated, and then the curve is divided into k equal pieces, where k is the number of the partitions. The selected data is thereafter partitioned into k groups corresponding to the curve divisions.

摘要翻译： 分区系统，提供快速，简单和灵活的分割数据集的方法。 在计算机系统内执行的过程从数据存储中检索产品和销售数据。 通过用户感兴趣的数据属性选择和排序数据项，并为所选择的数据和数据属性确定分布曲线。 计算分布曲线的总长度，然后将曲线划分为k个相等的部分，其中k是分区的数量。 然后将所选择的数据划分成与曲线分割对应的k个组。

公开(公告)号：US20080133313A1

公开(公告)日：2008-06-05

申请号：US11566357

申请日：2006-12-04

申请人： Arash Bateni ,  Edward Kim ,  Philip Liew ,  Jean-Philipe Vorsanger

发明人： Arash Bateni ,  Edward Kim ,  Philip Liew ,  Jean-Philipe Vorsanger

IPC分类号： G06Q10/00

CPC分类号： G06Q10/087 ,  G06Q10/04 ,  G06Q30/02 ,  G06Q30/0202 ,  G06Q30/0204

摘要： An improved method for forecasting and modeling product demand for a product. The forecasting methodology blends information about the future price of a product with historical sales data to better forecast the future product demand. This forecasting methodoloy takes into account three main parameters that may affect the future demand for a product: seasonality (using seasonal factors), recent sales trends (through average rate of sale analysis) and the product price (by estimating the price driven demand).

摘要翻译： 一种改进的产品需求预测和建模方法。 预测方法将有关产品未来价格的信息与历史销售数据相结合，以更好地预测未来的产品需求。 这种预测方法考虑了可能影响产品未来需求的三个主要参数：季节性（使用季节性因素），最近的销售趋势（通过平均销售率分析）和产品价格（通过估算价格驱动的需求）。

公开(公告)号：US08359229B2

公开(公告)日：2013-01-22

申请号：US11863958

申请日：2007-09-28

申请人： Arash Bateni ,  Edward Kim ,  Philip Liew ,  Jean-Philippe Vorsanger

发明人： Arash Bateni ,  Edward Kim ,  Philip Liew ,  Jean-Philippe Vorsanger

IPC分类号： G06Q40/00

CPC分类号： G06Q30/0202 ,  G06Q10/04 ,  G06Q30/02

摘要： An improved method for forecasting and modeling product demand for a product during promotional periods. The forecasting methodology employs information about prior promotional demand forecasts, prior product sales, and the data dispersion and the number of data samples in a product class hierarchy to dynamically determine the optimal level at which to compute promotional uplift coefficients. The methodology calculates confidence values for promotional uplift coefficients for products at each level in a merchandise product hierarchy, and uses the confidence values as a filter to determine the optimal level for promotional uplift aggregation.

摘要翻译： 一种在促销期间预测和建模产品需求的改进方法。 预测方法采用关于先前的促销需求预测，先前的产品销售以及产品类层次结构中的数据分散和数据样本的数量的信息来动态地确定计算促销隆起系数的最佳级别。 该方法计算商品产品层级中每个级别的产品的促销提升系数的置信度值，并使用置信度值作为过滤器来确定促销隆起聚合的最佳级别。

公开(公告)号：US07996254B2

公开(公告)日：2011-08-09

申请号：US11938812

申请日：2007-11-13

申请人： Arash Bateni ,  Edward Kim ,  Harminter Atwal ,  Jean-Philippe Vorsanger

发明人： Arash Bateni ,  Edward Kim ,  Harminter Atwal ,  Jean-Philippe Vorsanger

IPC分类号： G06Q99/00

CPC分类号： G06Q30/02 ,  G06Q10/04 ,  G06Q30/0202 ,  G06Q30/0206

摘要： An improved method for forecasting and modeling product demand for a product during promotional periods. The forecasting methodology employs a multivariable regression model to model the causal relationship between product demand and the attributes of past promotional activities. The model is utilized to calculate the promotional uplift from the coefficients of the regression equation. The methodology utilizes a mathematical formulation that transforms regression coefficients, a combination of additive and multiplicative coefficients, into a single promotional uplift coefficient that can be used directly in promotional demand forecasting calculations.

摘要翻译： 一种在促销期间预测和建模产品需求的改进方法。 预测方法采用多变量回归模型来模拟产品需求与过去促销活动的属性之间的因果关系。 该模型用于从回归方程的系数计算促销隆起。 该方法利用将回归系数（加法和乘法系数的组合）转换成可以在促销需求预测计算中直接使用的单个促销隆起系数的数学公式。

公开(公告)号：US20110153386A1

公开(公告)日：2011-06-23

申请号：US12644053

申请日：2009-12-22

申请人： Edward Kim ,  Arash Bateni

发明人： Edward Kim ,  Arash Bateni

IPC分类号： G06Q10/00 ,  G06F17/30

CPC分类号： G06Q10/04 ,  G06Q10/067 ,  G06Q30/0202

摘要： An improved method and system for forecasting product demand using a causal methodology, based on multiple regression techniques. The improved causal method revises product group seasonal factors used by conventional forecasting applications to best fit the sales pattern of an individual product in the product group through the calculation of an exponential coefficient which measures the deviation of the historical sales pattern of an individual product from the product group seasonal factors. The value of exponential coefficient is calculated using a causal framework through multivariable regression analysis.

摘要翻译： 基于多元回归技术，使用因果方法预测产品需求的改进方法和系统。 改进的因果方法通过计算指标系数​​来衡量常规预测应用中使用的产品组季节性因素，以最佳地适应产品组中单个产品的销售模式，该指数系数衡量单个产品的历史销售模式与 产品组季节性因素。 指数系数的值通过多变量回归分析的因果框架计算。

公开(公告)号：US20100169166A1

公开(公告)日：2010-07-01

申请号：US12649005

申请日：2009-12-29

申请人： Arash Bateni ,  Edward Kim ,  Philippe Hamel ,  Blazimir Radovic

发明人： Arash Bateni ,  Edward Kim ,  Philippe Hamel ,  Blazimir Radovic

IPC分类号： G06Q10/00 ,  G06F17/30

CPC分类号： G06Q30/02 ,  G06Q30/0202

摘要： An improved method for forecasting and modeling product demand for a product. The forecasting methodology employs a causal methodology, based on multiple regression techniques, to model the effects of various factors on product demand, and hence better forecast future patterns and trends, improving the efficiency and reliability of the inventory management systems. The improved method identifies linear dependent causal factors and removes redundant causal factors from the regression analysis. A product demand forecast is generated by blending forecast or expected values of the non-redundant causal factors together with corresponding regression coefficients determined through the analysis of historical product demand and factor information.

摘要翻译： 一种改进的产品需求预测和建模方法。 预测方法采用基于多元回归技术的因果方法来模拟各种因素对产品需求的影响，从而更好地预测未来模式和趋势，提高库存管理系统的效率和可靠性。 改进的方法识别线性相关因素，并从回归分析中消除重要的因果因素。 产品需求预测是通过将非冗余因果因子的预测值或预期值与通过分析历史产品需求和因子信息确定的相应回归系数相结合而产生的。

公开(公告)号：US20100138274A1

公开(公告)日：2010-06-03

申请号：US12326145

申请日：2008-12-02

申请人： Arash Bateni ,  Edward Kim

发明人： Arash Bateni ,  Edward Kim

IPC分类号： G06F17/30

CPC分类号： G06Q30/02 ,  G06Q10/04 ,  G06Q30/0202

摘要： A product demand forecasting methodology is presented that applies daily weight values to a weekly forecast to determine daily forecasts for a product or service. The method determines daily weight values for use in forecasting current product sales by blending daily weight values calculated from historical demand data for both recent weeks and year-prior weeks. Recent weeks are used to account for recent correlations and alternation effects, and year-prior weeks are used to account for seasonality effects. The method automatically calculates a measure of significance for the daily weights calculated from the recent weeks and year-prior weeks. The significance of each week is applied as a weighting factor during the blending of recent weeks and year-prior daily weight values.

摘要翻译： 提出了一种产品需求预测方法，将每日重量值应用于每周预测，以确定产品或服务的每日预测。 该方法通过混合最近几周和前一周的历史需求数据计算的每日重量值，确定用于预测当前产品销售的每日重量值。 最近几周用于说明最近的相关性和交替效应，并且前一周用于考虑季节效应。 该方法自动计算从最近几周和前一周计算的每日重量的重要度量度。 在最近几周和前一天每日体重值的混合期间，每周的意义被用作加权因子。

公开(公告)号：US08560374B2

公开(公告)日：2013-10-15

申请号：US12326145

申请日：2008-12-02

申请人： Arash Bateni ,  Edward Kim

发明人： Arash Bateni ,  Edward Kim

IPC分类号： G06Q30/02

CPC分类号： G06Q30/02 ,  G06Q10/04 ,  G06Q30/0202

摘要： A product demand forecasting methodology is presented that applies daily weight values to a weekly forecast to determine daily forecasts for a product or service. The method determines daily weight values for use in forecasting current product sales by blending daily weight values calculated from historical demand data for both recent weeks and year-prior weeks. Recent weeks are used to account for recent correlations and alternation effects, and year-prior weeks are used to account for seasonality effects. The method automatically calculates a measure of significance for the daily weights calculated from the recent weeks and year-prior weeks. The significance of each week is applied as a weighting factor during the blending of recent weeks and year-prior daily weight values.

摘要翻译： 提出了一种产品需求预测方法，将每日重量值应用于每周预测，以确定产品或服务的每日预测。 该方法通过混合最近几周和前一周的历史需求数据计算的每日重量值，确定用于预测当前产品销售的每日重量值。 最近几周用于说明最近的相关性和交替效应，并且前一周用于考虑季节效应。 该方法自动计算从最近几周和前一周计算的每日重量的重要度量度。 在最近几周和前一天每日体重值的混合期间，每周的意义被用作加权因子。