Optimizing machine learning model total payment volume predictions using model stacking
US11531946B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 30, 2020 |
| Grant date | Dec 20, 2022 |
| Priority date | — |
| Expiry date | Aug 29, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG08B21/182
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
This specification includes machine learning model stacking techniques allowing for greater predictive accuracy using disparate sources of data. In one embodiment, a system obtains TPV data and inputs the TPV data into a forecasting model. Based on the total payment volume data, the forecasting model may output a first prediction of a total payment volume for a future period of time. The system may acquire prediction enhancing data and input the first prediction from the forecasting model and the acquired prediction enhancing data into a machine learning model. Based on the first prediction and the acquired prediction enhancing data, the machine learning model may output a second prediction of the total payment volume for the future period of time. The second prediction may be compared against real-time TPV and determined differences may be used for controlling operations of various machines system/network environment machines.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.