- 专利标题: Selecting forecasting model complexity using eigenvalues
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申请号: US14195954申请日: 2014-03-04
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公开(公告)号: US10469398B2公开(公告)日: 2019-11-05
- 发明人: Aaron K. Baughman , Guillermo A. Cecchi , James R. Kozloski , Brian M. O'Connell
- 申请人: International Business Machines Corporation
- 申请人地址: US NY Armonk
- 专利权人: INTERNATIONAL BUSINESS MACHINES CORPORATION
- 当前专利权人: INTERNATIONAL BUSINESS MACHINES CORPORATION
- 当前专利权人地址: US NY Armonk
- 代理机构: Garg Law Firm, PLLC
- 代理商 Rakesh Garg; James Nock
- 主分类号: H04L12/911
- IPC分类号: H04L12/911 ; G06Q10/04
摘要:
A method, system, and computer program product for selecting forecasting model complexity using eigenvalues are provided in the illustrative embodiments A process is represented in a model. The model comprises a mathematical representation of the process in a certain degree. A first portion of historical data generated by the process during a first period is selected and includes an actual value of an outcome of the process and a value of a feature influencing the process during the first period. A prediction is made of a predicted value of the outcome. A difference between the prediction and the actual value of the outcome is determined. The difference is represented as a change in a distribution of eigenvalues. According to the change, a second model is to represent the process. The second model comprises a second mathematical representation of the process in a different degree.
公开/授权文献
- US20150254569A1 SELECTING FORECASTING MODEL COMPLEXITY USING EIGENVALUES 公开/授权日:2015-09-10
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