Time-series representation learning via random time warping
US11366990B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 15, 2017 |
| Grant date | Jun 21, 2022 |
| Priority date | — |
| Expiry date | Oct 29, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments of the present invention provide a computer-implemented method for performing unsupervised time-series feature learning. The method generates a set of reference time-series of random lengths, in which each length is uniformly sampled from a predetermined minimum length to a predetermined maximum length, and in which values of each reference time-series in the set are drawn from a distribution. The method generates a feature matrix for raw time-series data based on a set of computed distances between the generated set of reference time-series and the raw time-series data. The method provides the feature matrix as an input to one or more machine learning models.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.