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Kloudfuse platform provides ability to perform anomaly detection on underlying data. Anomaly monitoring is effective when looking out for deviation in behavior in comparison to its past behavior. For example, are # of requests unusually different then it’s past behavior. Kloudfuse provides following algorithms for outlier anomaly detection:
Basic (Rolling-Quantile) is a statistical learning algorithm used to detect anomaly based on earlier behavior measured by the specified quantile. Following parameters are provided to tune the algorithm:
Threshold: # of standard deviations the value have to be away from mean for it to be considered anomalous. For example, a value of 1 would indicate 0.68 quantile value.
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