Gradient learning for probabilistic ARMA time-series models
US7421380B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 14, 2004 |
| Grant date | Sep 2, 2008 |
| Priority date | — |
| Expiry date | Sep 15, 2026 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2218/08
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The subject invention leverages the conditional Gaussian (CG) nature of a continuous variable stochastic ARMAxp time series model to efficiently determine its parametric gradients. The determined gradients permit an easy means to construct a parametric structure for the time series model. This provides a gradient-based alternative to the expectation maximization (EM) process for learning parameters of the stochastic ARMAxp time series model. Thus, gradients for parameters can be computed and utilized with a gradient-based learning method for estimating the parameters. This allows values of continuous observations in a time series to be predicted utilizing the stochastic ARMAxp time series model, providing efficient and accurate predictions.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.