Plato anomaly detection
Abstract:
A method for continuous data anomaly detection includes identifying a period of time covered by metrics data stored in a repository. The stored metrics data is categorized into a plurality of non-overlapping time segments. Statistical analysis of the stored metrics data is performed based on the identified period of time. A range of acceptable metric values is dynamically generated based on the performed statistical analysis.
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