Documentation

anomalydetection.mad() function

anomalydetection.mad() is a user-contributed function maintained by the package author.

anomalydetection.mad() uses the median absolute deviation (MAD) algorithm to detect anomalies in a data set.

Input data requires _time and _value columns. Output data is grouped by _time and includes the following columns of interest:

  • _value: difference between of the original _value from the computed MAD divided by the median difference.
  • MAD: median absolute deviation of the group.
  • level: anomaly indicator set to either anomaly or normal.
Function type signature
(<-table: stream[B], ?threshold: A) => stream[{C with level: string, _value_diff_med: D, _value_diff: D, _value: D}] where A: Comparable + Equatable, B: Record, D: Comparable + Divisible + Equatable
For more information, see Function type signatures.

Parameters

threshold

Deviation threshold for anomalies.

table

Input data. Default is piped-forward data (<-).

Examples

Use the MAD algorithm to detect anomalies

import "contrib/anaisdg/anomalydetection"
import "sampledata"

sampledata.float()
    |> anomalydetection.mad(threshold: 1.0)

View example input and output


Was this page helpful?

Thank you for your feedback!


Linux Package Signing Key Rotation

All signed InfluxData Linux packages have been resigned with an updated key. If using Linux, you may need to update your package configuration to continue to download and verify InfluxData software packages.

For more information, see the Linux Package Signing Key Rotation blog post.

State of the InfluxDB Cloud (IOx) documentation

The new documentation for InfluxDB Cloud backed by InfluxDB IOx is a work in progress. We are adding new information and content almost daily. Thank you for your patience!

If there is specific information you’re looking for, please submit a documentation issue.