Unsupervised anomaly detection for arbitrary time series

US9652354B2 · kind B2 · utility

Assignee

• MICROSOFT TECHNOLOGY LICENSING, LLC · US

Inventors

• Vitaly Filimonov · Bothell, US
• Panagiotis Periorellis · Munich, DE
• Dmitry Starostin · Aachen, DE
• Alexandre de Baynast · Aachen, DE
• Eldar Akchurin · Aachen, DE
• Aleksandr Klimov · Moscow, RU
• Thomas Minka · Cambridge, GB
• Alexander Armin Spengler · Cambridge, GB

Key dates

• Filing date: Mar 18, 2014
• Grant date: May 16, 2017
• Expiry date: Jun 30, 2035

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N20/20
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Examining time series sequences representing performance counters from executing programs can provide significant clues about potential malfunctions, busy periods in terms of traffic on networks, intensive processing cycles and so on. An unsupervised anomaly detector can detect anomalies for any time series. A combination of known techniques from statistics, signal processing and machine learning can be used to identify outliers on unsupervised data, and to capture anomalies like edge detection, spike detection, and pattern error anomalies. Boolean and probabilistic results concerning whether an anomaly was detected can be provided.