Systems and methods for modeling continuous stochastic processes with dynamic normalizing flows
US12248865B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 8, 2021 |
| Grant date | Mar 11, 2025 |
| Priority date | — |
| Expiry date | Nov 13, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/044
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods for machine learning architecture for time series data prediction. The system may include a processor and a memory storing processor-executable instructions. The processor-executable instructions, when executed, may configure the processor to: obtain time series data associated with a data query; generate a predicted value based on a sampled realization of the time series data and a continuous time generative model, the continuous time generative model trained to define an invertible mapping to maximize a log-likelihood of a set of predicted values for a time range associated with the time series data; and generate a signal providing an indication of the predicted value associated with the data query.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.