Invention Grant
- Patent Title: Selecting forecasting model complexity using eigenvalues
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Application No.: US14195954Application Date: 2014-03-04
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Publication No.: US10469398B2Publication Date: 2019-11-05
- Inventor: Aaron K. Baughman , Guillermo A. Cecchi , James R. Kozloski , Brian M. O'Connell
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Current Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Current Assignee Address: US NY Armonk
- Agency: Garg Law Firm, PLLC
- Agent Rakesh Garg; James Nock
- Main IPC: H04L12/911
- IPC: H04L12/911 ; G06Q10/04

Abstract:
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.
Public/Granted literature
- US20150254569A1 SELECTING FORECASTING MODEL COMPLEXITY USING EIGENVALUES Public/Granted day:2015-09-10
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