Automated machine learning pipeline for timeseries datasets utilizing point-based algorithms
US11989657B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 15, 2020 |
| Grant date | May 21, 2024 |
| Priority date | — |
| Expiry date | Mar 19, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Herein, a computer generates and evaluates many preprocessor configurations for a window preprocessor that transforms a training timeseries dataset for an ML model. With each preprocessor configuration, the window preprocessor is configured. The window preprocessor then converts the training timeseries dataset into a configuration-specific point-based dataset that is based on the preprocessor configuration. The ML model is trained based on the configuration-specific point-based dataset to calculate a score for the preprocessor configuration. Based on the scores of the many preprocessor configurations, an optimal preprocessor configuration is selected for finally configuring the window preprocessor, after which, the window preprocessor can optimally transform a new timeseries dataset such as in an offline or online production environment such as for real-time processing of a live streaming timeseries.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.