Bayesian approach for learning regression decision graph models and regression models for time series analysis
US7660705B1 · kind B1 · utility
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24References
44Claims
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Key dates
| Filing date | Mar 19, 2002 |
| Grant date | Feb 9, 2010 |
| Priority date | — |
| Expiry date | Aug 3, 2024 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/295
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and systems are disclosed for learning a regression decision graph model using a Bayesian model selection approach. In a disclosed aspect, the model structure and/or model parameters can be learned using a greedy search algorithm applied to grow the model so long as the model improves. This approach enables construction of a decision graph having a model structure that includes a plurality of leaves, at least one of which includes a non-trivial linear regression. The resulting model thus can be employed for forecasting, such as for time series data, which can include single or multi-step forecasting.
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