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arXiv · 1704.05325

Anomaly detection and motif discovery in symbolic representations of time series

Fabio Guigou·Pierre Collet·Pierre Parrend
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Abstract

The advent of the Big Data hype and the consistent recollection of event logs and real-time data from sensors, monitoring software and machine configuration has generated a huge amount of time-varying data in about every sector of the industry. Rule-based processing of such data has ceased to be relevant in many scenarios where anomaly detection and pattern mining have to be entirely accomplished by the machine. Since the early 2000s, the de-facto standard for representing time series has been the Symbolic Aggregate approXimation (SAX).In this document, we present a few algorithms using this representation for anomaly detection and motif discovery, also known as pattern mining, in such data. We propose a benchmark of anomaly detection algorithms using data from Cloud monitoring software.

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Fabio Guigou, Pierre Collet, Pierre Parrend. 2017-04-18. Anomaly detection and motif discovery in symbolic representations of time series. https://doi.org/10.13140/rg.2.2.20158.69447

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