Product Features

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Analytics

Statistical Functions

# Anomaly Detection

1min

Anomaly Detection uses the three-sigma rule and allows you to define the number of standard deviations considered normal for a data-set. If the data is within the designated standard deviations of the mean, it is not considered to be anomalous.

When creating an analytics flow, refer to details about this function processor by reviewing Processor Flow Parameters.

The following information describes this Function:

- Anomaly detection, using the three-Sigma Rule, is a conventional heuristic used for an approximately normalized data-set.
- A rolling window of values are observed, and their average and standard deviations are calculated.
- For
**Deviations**, you can define the number of standard deviations that would be considered normal, making everything outside that definition anomalous data. - In the case of an anomaly, the standard deviation window is expanded ever so slightly so that it can adjust if this anomalous data becomes seasonal.
**Window Size**determines how many seconds of data you want to use for anomaly detection.- For
**Expected Output Fields**: timestamp, current value, moving average, moving standard deviation, upper limit, lower limit, total anomalies, and anomaly field replaces current value, if detected.

Output fields

Total Anomalies