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1.
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
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.
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2.
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
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.
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