Machine learning-based anomaly detection using time series decomposition
US11803773B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 30, 2019 |
| Grant date | Oct 31, 2023 |
| Priority date | — |
| Expiry date | May 29, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F11/3072
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods, apparatus, and processor-readable storage media for machine learning-based anomaly detection using time series decomposition are provided herein. An example computer-implemented method includes processing, via machine learning techniques pertaining to time series decomposition functions, a first set of historical time series data derived from multiple systems within an enterprise; generating, based on the processed data, one or more pairs of upper bounds and lower bounds directed to system metrics; identifying system anomalies attributed to one or more of the multiple systems within the enterprise by comparing a second set of historical time series data derived from the one or more systems against the one or more pairs of upper bounds and lower bounds; prioritizing, via machine learning techniques pertaining to weighting functions, the system anomalies; and outputting, in accordance with the prioritization, the system anomalies to a user within the enterprise.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.