Techniques for prediction models using time series data
US11894971B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 31, 2021 |
| Grant date | Feb 6, 2024 |
| Priority date | — |
| Expiry date | Mar 31, 2041 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L43/067
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
Various aspects involve a lagged prediction model trained for risk assessment or other purposes. For instance, a risk assessment computing system receives a risk assessment query for a target entity and provides an input predictor record for the target entity to a lagged prediction model. The input predictor record includes a first group of lagged values from a first time-series attribute associated with the target entity. The lagged prediction model is trained by implementing a group feature selection technique configured to select the first time-series attribute as input and to deselect a second time-series attribute associated with the target entity. The risk assessment computing system computes an output risk indicator from the input predictor record and transmits the output risk indicator to a remote computing system. The output risk indicator can be used to control access by the target entity to one or more interactive computing environments.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.