公开(公告)号：USD915458S1

公开(公告)日：2021-04-06

申请号：US29674055

申请日：2018-12-19

Applicant: SAS Institute Inc.

Designer： Michael Ryan Chipley ,  Steven Todd Barlow

公开(公告)号：USD915459S1

公开(公告)日：2021-04-06

申请号：US29674059

申请日：2018-12-19

Applicant: SAS Institute Inc.

Designer： Michael Ryan Chipley ,  Steven Todd Barlow

公开(公告)号：US20130238399A1

公开(公告)日：2013-09-12

申请号：US13772200

申请日：2013-02-20

Applicant: SAS INSTITUTE INC.

Inventor： Michael Ryan Chipley ,  Michael J. Leonard ,  Philip Lodge Holman ,  Jerzy Michal Brzezicki ,  Karl Moss ,  Dinesh P. Apte

IPC: G06Q10/06

CPC classification number: G06Q10/06375 ,  G06Q10/06 ,  G06Q10/067

Abstract: Computer-implemented systems and methods are provided for implementing a scenario analysis manager that performs multiple scenarios based upon time series data that is representative of transactional data are provided. A system and method provides candidate predictive models for a first scenario for selection where the set of candidate predictive models includes an identification of variables associated with a model. Model selection data is received from a scenario analysis manager where a selected model is configured to predict a future value of a first variable based on values of a second variable. Time series data is received representative of past transaction activity of the first variable and the second variable, and data representative of a future value of the second variable is also received. The future value of the first variable is determined using the selected model, the time-series data and the future value of the second variable.

Abstract translation: 提供了计算机实现的系统和方法，用于实现基于提供代表事务数据的时间序列数据执行多个场景的情景分析管理器。 系统和方法提供用于选择的第一场景的候选预测模型，其中候选预测模型集合包括与模型相关联的变量的标识。 从场景分析管理器接收模型选择数据，其中所选择的模型被配置为基于第二变量的值来预测第一变量的未来值。 接收代表第一变量和第二变量的过去交易活动的时间序列数据，并且还接收表示第二变量的未来值的数据。 使用所选择的模型，时间序列数据和第二变量的未来值来确定第一变量的未来值。

公开(公告)号：US10482376B1

公开(公告)日：2019-11-19

申请号：US16225741

申请日：2018-12-19

Applicant: SAS Institute Inc.

Inventor： Amrut Shantaram Vaze ,  Michael Ryan Chipley ,  Leigh Anne Ward ,  Ashish Mishra ,  Steven Todd Barlow ,  Suchitra Balaso Chikhalkar ,  Sameer Waman Tatke

IPC: G06F15/18 ,  G06N3/08 ,  G06N20/00 ,  G06N5/02

Abstract: The computing device generates a classification model providing prediction data indicating predicted users in a target population who will respond to a target stimulus according to a predefined user response category. The computing device displays in GUI a graphical representation of a generated classification model and a plurality of options each specifying one of different objectives for determining a proportion of users in the target population to expose to the target stimulus. The computing device predicts proportion data indicating the proportion of users in the target population to expose to the target stimulus based on the determined location of the cut-off. The computing device issues one or more indications as to whether to use the classification model as a basis for exposing the proportion of users in the target population to the target stimulus according to the proportion data.

公开(公告)号：US20150120263A1

公开(公告)日：2015-04-30

申请号：US14557312

申请日：2014-12-01

Applicant: SAS Institute Inc.

Inventor： Jerzy Michal Brzezicki ,  Dinesh P. Apte ,  Michael J. Leonard ,  Michael Ryan Chipley ,  Sagar Arun Mainkar ,  Edward Tilden Blair

IPC: G06F17/50

CPC classification number: G06N7/005 ,  G06F17/18 ,  G06F17/5009 ,  G06F2217/10 ,  G16B40/00

Abstract: Systems and methods are provided for evaluating performance of forecasting models. A plurality of forecasting models may be generated using a set of in-sample data. Two or more forecasting models from the plurality of forecasting models may be selected for use in generating a combined forecast. An ex-ante combined forecast may be generated for an out-of-sample period using the selected two or more forecasting models. The ex-ante combined forecast may then be compared with a set of actual out-of-sample data to evaluate performance of the combined forecast.

Abstract translation: 提供了系统和方法来评估预测模型的性能。 可以使用一组样本内数据来生成多个预测模型。 可以选择来自多个预测模型的两个或更多个预测模型用于生成组合预测。 可以使用所选择的两个或多个预测模型，针对采样外期间产生事前组合预测。 然后将事前综合预测与一组实际样本数据进行比较，以评估综合预测的绩效。