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Diffstat (limited to 'collectors/python.d.plugin/zscores/README.md')
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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 |