---
title: anomalydetection.mad() function
description: anomalydetection.mad() uses the median absolute deviation (MAD) algorithm to detect anomalies in a data set.
url: https://docs.influxdata.com/flux/v0/stdlib/contrib/anaisdg/anomalydetection/mad/
estimated_tokens: 2047
product: Flux
version: v0
---

# anomalydetection.mad() function

-   Flux 0.90.0+
-   View InfluxDB support

`anomalydetection.mad()` is a user-contributed function maintained by the [package author](#package-author-and-maintainer).

`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

```js
(<-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](/flux/v0/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

```js
import "contrib/anaisdg/anomalydetection"
import "sampledata"

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

[](#view-example-input-and-output)

View example input and output

#### Input data

| _time | *tag | _value |
| --- | --- | --- |
| 2021-01-01T00:00:00Z | t1 | -2.18 |
| 2021-01-01T00:00:10Z | t1 | 10.92 |
| 2021-01-01T00:00:20Z | t1 | 7.35 |
| 2021-01-01T00:00:30Z | t1 | 17.53 |
| 2021-01-01T00:00:40Z | t1 | 15.23 |
| 2021-01-01T00:00:50Z | t1 | 4.43 |

| _time | *tag | _value |
| --- | --- | --- |
| 2021-01-01T00:00:00Z | t2 | 19.85 |
| 2021-01-01T00:00:10Z | t2 | 4.97 |
| 2021-01-01T00:00:20Z | t2 | -3.75 |
| 2021-01-01T00:00:30Z | t2 | 19.77 |
| 2021-01-01T00:00:40Z | t2 | 13.86 |
| 2021-01-01T00:00:50Z | t2 | 1.86 |

#### Output data

| MAD | *_time | _value | _value_diff | _value_diff_med | level | tag |
| --- | --- | --- | --- | --- | --- | --- |
| 16.330839 | 2021-01-01T00:00:00Z | 1 | 11.015 | 11.015 | anomaly | t1 |
| 16.330839 | 2021-01-01T00:00:00Z | 1 | 11.015 | 11.015 | anomaly | t2 |

| MAD | *_time | _value | _value_diff | _value_diff_med | level | tag |
| --- | --- | --- | --- | --- | --- | --- |
| 4.410735 | 2021-01-01T00:00:10Z | 0.9999999999999999 | 2.9749999999999996 | 2.975 | normal | t1 |
| 4.410735 | 2021-01-01T00:00:10Z | 1.0000000000000002 | 2.9750000000000005 | 2.975 | anomaly | t2 |

| MAD | *_time | _value | _value_diff | _value_diff_med | level | tag |
| --- | --- | --- | --- | --- | --- | --- |
| 8.22843 | 2021-01-01T00:00:20Z | 1 | 5.55 | 5.55 | anomaly | t1 |
| 8.22843 | 2021-01-01T00:00:20Z | 1 | 5.55 | 5.55 | anomaly | t2 |

| MAD | *_time | _value | _value_diff | _value_diff_med | level | tag |
| --- | --- | --- | --- | --- | --- | --- |
| 1.6605119999999987 | 2021-01-01T00:00:30Z | 0.9999999999999984 | 1.1199999999999974 | 1.1199999999999992 | normal | t1 |
| 1.6605119999999987 | 2021-01-01T00:00:30Z | 1.0000000000000016 | 1.120000000000001 | 1.1199999999999992 | anomaly | t2 |

| MAD | *_time | _value | _value_diff | _value_diff_med | level | tag |
| --- | --- | --- | --- | --- | --- | --- |
| 1.0155810000000007 | 2021-01-01T00:00:40Z | 1 | 0.6850000000000005 | 0.6850000000000005 | anomaly | t1 |
| 1.0155810000000007 | 2021-01-01T00:00:40Z | 1 | 0.6850000000000005 | 0.6850000000000005 | anomaly | t2 |

| MAD | *_time | _value | _value_diff | _value_diff_med | level | tag |
| --- | --- | --- | --- | --- | --- | --- |
| 1.9051409999999995 | 2021-01-01T00:00:50Z | 1 | 1.2849999999999997 | 1.2849999999999997 | anomaly | t1 |
| 1.9051409999999995 | 2021-01-01T00:00:50Z | 1.0000000000000002 | 1.285 | 1.2849999999999997 | anomaly | t2 |