Selecting forecasting models by machine learning based on analysis of model robustness
US11475332B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 12, 2020 |
| Grant date | Oct 18, 2022 |
| Priority date | — |
| Expiry date | Jun 23, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q10/04
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computer-implemented method, a computer program product, and a computer system for selecting predictions by models. A computer receives a request for a forecast of a dependent variable in a time domain, where the time domain includes first time periods that have normal labels due to normal predictor variable data and second time periods that have anomalous labels due to anomalous predictor variable data. The computer retrieves accuracy scores and robustness scores of models, where the accuracy scores indicate forecasting accuracy in the first time periods and the robustness scores indicate forecasting accuracy in the second time periods. For predictions in the first time period, the computer selects dependent variable values predicted by a first model that has highest values of the accuracy scores. For predictions in the second time periods, the computer selects dependent variable values predicted by a second model that has highest values of the robustness scores.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.