Computerized methods and systems for machine-learned multi-output multi-step forecasting of time-series data
US12118061B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 20, 2023 |
| Grant date | Oct 15, 2024 |
| Priority date | — |
| Expiry date | Jul 20, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG16H50/30
- WIPO fieldMedical technology
- WIPO sectorInstruments
Abstract
A method includes, in response to receiving a forecasting request from a user device via a web portal, determining a measure of uncertainty associated with an input vector specified by the forecasting request. The measure of uncertainty includes a compact representation of uncertainty associated with future time intervals specified by the forecasting request. The method includes obtaining a set of historical data from a time-series data store. The method includes generating a forecast model using the set of historical data, the input vector, and the measure of uncertainty to predict outcomes incrementally for the future time intervals. The method includes determining a predicted outcome using the forecast model at an end of the future time intervals. The method includes, in response to the predicted outcome exceeding a threshold, generating a graphical user interface. The graphical user interface illustrates the mean vector and a magnitude of the predicted uncertainty measure.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.