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| author | Daniel Baumann <daniel.baumann@progress-linux.org> | 2023-02-06 16:11:34 +0000 |
|---|---|---|
| committer | Daniel Baumann <daniel.baumann@progress-linux.org> | 2023-02-06 16:11:34 +0000 |
| commit | d079b656b4719739b2247dcd9d46e9bec793095a (patch) | |
| tree | d2c950c70a776bcf697c963151c5bd959f8a9f03 /collectors/python.d.plugin/zscores/README.md | |
| parent | Releasing debian version 1.37.1-2. (diff) | |
| download | netdata-d079b656b4719739b2247dcd9d46e9bec793095a.tar.xz netdata-d079b656b4719739b2247dcd9d46e9bec793095a.zip | |
Merging upstream version 1.38.0.
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
Diffstat (limited to 'collectors/python.d.plugin/zscores/README.md')
| -rw-r--r-- | collectors/python.d.plugin/zscores/README.md | 8 |
1 files changed, 6 insertions, 2 deletions
diff --git a/collectors/python.d.plugin/zscores/README.md b/collectors/python.d.plugin/zscores/README.md index 4f84a6c1f..d89aa6a0f 100644 --- a/collectors/python.d.plugin/zscores/README.md +++ b/collectors/python.d.plugin/zscores/README.md @@ -1,14 +1,18 @@ <!-- title: "zscores" description: "Use statistical anomaly detection to narrow your focus and shorten root cause analysis." -custom_edit_url: https://github.com/netdata/netdata/edit/master/collectors/python.d.plugin/zscores/README.md +custom_edit_url: "https://github.com/netdata/netdata/edit/master/collectors/python.d.plugin/zscores/README.md" +sidebar_label: "zscores" +learn_status: "Published" +learn_topic_type: "References" +learn_rel_path: "References/Collectors references/Uncategorized" --> # Z-Scores - basic anomaly detection for your key metrics and charts Smoothed, rolling [Z-Scores](https://en.wikipedia.org/wiki/Standard_score) for selected metrics or charts. -This collector uses the [Netdata rest api](https://learn.netdata.cloud/docs/agent/web/api) to get the `mean` and `stddev` +This collector uses the [Netdata rest api](https://github.com/netdata/netdata/blob/master/web/api/README.md) to get the `mean` and `stddev` for each dimension on specified charts over a time range (defined by `train_secs` and `offset_secs`). For each dimension it will calculate a Z-Score as `z = (x - mean) / stddev` (clipped at `z_clip`). Scores are then smoothed over time (`z_smooth_n`) and, if `mode: 'per_chart'`, aggregated across dimensions to a smoothed, rolling chart level Z-Score |