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Time Series Anomaly Detection in Devo

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Time Series Anomaly Detection (TSAD) is the process of detecting abnormal behavior – anomalies – in time series data. Time series data is data that captures the value of a metric at a point of time – for example, number of errors in a TV stream, CPU utilization of a server, temperature recorded by a data center sensor, and more.

Devo solves the machine-scale TSAD problem by applying Machine Learning (ML) techniques to learn unique profiles of what is “normal” per metric being measured.

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