Anomaly detection using unsupervised learning and surrogate data sets

US11831525B1 · kind B1 · utility

Assignee

• SAP SE · DE

Inventors

• Arndt Effern · Steinsfurt, DE
• Maximilian Schmidtke · Sinsheim, DE
• Erkan Emre · Hockenheim, DE

Key dates

• Filing date: Mar 28, 2023
• Grant date: Nov 28, 2023
• Expiry date: Mar 28, 2043

Classification

• Technology area (CPC H): Electricity
• CPC primary: H04L43/0894
• WIPO field: Digital communication
• WIPO sector: Electrical engineering

Abstract

Systems and methods include determination of training data instances associated with a respective time periods based on time-series data of each of several metrics, training of a score generator, based on the training data instances, to generate an outlier score, generation of surrogate time-series data of each of the metrics based on the time-series data of each of the metrics, determination of input data instances associated with each one of the respective time periods based on the surrogate time-series data, input of the input data instances to the trained score generator to generate an outlier score for each input data instance, determination of a threshold based on the outlier scores, identification of ones of the training data instances associated with an outlier score greater than the threshold, and identification of an anomaly associated with each of the training data instances associated with an outlier score greater than the threshold.