Computer-implemented system and method for relational time series learning
US10438130B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 1, 2015 |
| Grant date | Oct 8, 2019 |
| Priority date | — |
| Expiry date | Aug 7, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
System and methods for relational time-series learning are provided. Unlike traditional time series forecasting techniques, which assume either complete time series independence or complete dependence, the disclosed system and method allow time series forecasting that can be performed on multivariate time series represented as vertices in graphs with arbitrary structures and predicting a future classification for data items represented by one of nodes in the graph. The system and methods also utilize non-relational, relational, temporal data for classification, and allow using fast and parallel classification techniques with linear speedups. The system and methods are well-suited for processing data in a streaming or online setting and naturally handle training data with skewed or unbalanced class labels.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.