Scalable bootstrap method for assessing the quality of machine learning algorithms over massive time series
US9530104B1 · kind B1 · utility
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Key dates
| Filing date | Feb 7, 2014 |
| Grant date | Dec 27, 2016 |
| Priority date | — |
| Expiry date | Mar 14, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Described is a system for assessing the quality of machine learning algorithms over massive time series. A set of random blocks of a time series data sample of size n is selected in parallel. Then, r resamples are generated, in parallel, by applying a bootstrapping method to each block in the set of random blocks to obtain a resample of size n, where r is not fixed. Errors are estimated on the r resamples, and a final accuracy estimate is produced by averaging the errors estimated on the r resamples.
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