| title |
description |
menu |
weight |
| anomalydetection.mad() function |
`anomalydetection.mad()` uses the median absolute deviation (MAD) algorithm to detect anomalies in a data set.
|
| flux_v0_ref |
| name |
parent |
identifier |
| anomalydetection.mad |
contrib/anaisdg/anomalydetection |
contrib/anaisdg/anomalydetection/mad |
|
|
301 |
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
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For more information, see Function type signatures.
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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)
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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 |
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