公开(公告)号：US10540377B2

公开(公告)日：2020-01-21

申请号：US16379470

申请日：2019-04-09

Applicant: SAS Institute Inc.

Inventor： Yue Li ,  Neha Bindumadhav Kulkarni ,  Yung-Hsin Chien ,  Sagar Arun Mainkar ,  Bhupendra Suresh Bendale

IPC: G06F16/00 ,  G06F16/28 ,  G06F16/22

Abstract: A hierarchical structure (e.g., a hierarchy) for use in hierarchical analysis (e.g., hierarchical forecasting) of timestamped data can be automatically generated. This automated approach to determining a hierarchical structure involves identifying attributes of the timestamped data, clustering the timestamped data to select attributes for the hierarchy, ordering the attributes to achieve a recommended hierarchical order, and optionally modifying the hierarchical order based on user input. Through the approach disclosed herein, a hierarchy can be generated that is designed to perform well under hierarchical models. This recommended hierarchy for use in hierarchical analysis may be agnostic to any planned hierarchy provided by or used by a user to otherwise interpret the timestamped 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。

公开(公告)号：US20190317952A1

公开(公告)日：2019-10-17

申请号：US16379470

申请日：2019-04-09

Applicant: SAS Institute Inc.

Inventor： Yue Li ,  Neha Bindumadhav Kulkarni ,  Yung-Hsin Chien ,  Sagar Arun Mainkar ,  Bhupendra Suresh Bendale

IPC: G06F16/28 ,  G06F16/22

Abstract: A hierarchical structure (e.g., a hierarchy) for use in hierarchical analysis (e.g., hierarchical forecasting) of timestamped data can be automatically generated. This automated approach to determining a hierarchical structure involves identifying attributes of the timestamped data, clustering the timestamped data to select attributes for the hierarchy, ordering the attributes to achieve a recommended hierarchical order, and optionally modifying the hierarchical order based on user input. Through the approach disclosed herein, a hierarchy can be generated that is designed to perform well under hierarchical models. This recommended hierarchy for use in hierarchical analysis may be agnostic to any planned hierarchy provided by or used by a user to otherwise interpret the timestamped data.