github-mirrors/VictoriaMetrics
1
0
Fork 0
mirror of https://github.com/VictoriaMetrics/VictoriaMetrics.git synced 2024-11-21 14:44:00 +00:00
Code Issues Projects Releases Packages Wiki Activity

docs/vmanomaly: updated model list in Overview (#6832)

Browse source 
### Describe Your Changes

Updated model list in Anomaly Detection Overview

### Checklist

The following checks are **mandatory**:

- [x] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).

(cherry picked from commit 7279899a8a)
This commit is contained in:
Daria Karavaieva 2024-08-17 12:43:28 +02:00 committed by hagen1778
parent c57e68a0cd
commit fa820bd9cf
No known key found for this signature in database
GPG key ID: 3BF75F3741CA9640
1 changed files with 16 additions and 1 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

17
docs/anomaly-detection/Overview.md
View file

@ -96,12 +96,27 @@ Currently, vmanomaly ships with a set of built-in models:
The algorithm has a linear time complexity and a low memory requirement, which works well with high-volume data. See [scikit-learn.org documentation](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html) for Isolation Forest.
1. [**MAD (Median Absolute Deviation)**](https://docs.victoriametrics.com/anomaly-detection/components/models/#mad-median-absolute-deviation)
A robust method for anomaly detection that is less sensitive to outliers in data compared to standard deviation-based models. It considers a point as an anomaly if the absolute deviation from the median is significantly large.
1. [**AutoTuned**](https://docs.victoriametrics.com/anomaly-detection/components/models/#autotuned)
Designed to take the cognitive load off the user, allowing any of built-in models below to be re-tuned for best params on data seen during each `fit` phase of the algorithm. Tradeoff is between increased computational time and optimized results / simpler maintenance.
1. [**Online Z-score**](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-z-score)
[Online](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-models) alternative to [Z-score](https://docs.victoriametrics.com/anomaly-detection/components/models/#z-score) model with exact same behavior and use cases.
1. [**Online MAD**](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-mad)
Approximate [online](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-models) alternative to [MAD model](https://docs.victoriametrics.com/anomaly-detection/components/models/#mad-median-absolute-deviation), appropriate for the same use cases.
1. [**Online Seasonal Quantile**](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-seasonal-quantile)
Best used on [de-trended](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#trend) data with strong (possibly multiple) [seasonalities](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#seasonality). Can act as a (slightly less powerful) [online](#online-models) replacement to [`ProphetModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#prophet).
> Note: The full list of models can be found [here](https://docs.victoriametrics.com/anomaly-detection/components/models/#overview)
### Examples
For example, here’s how Prophet predictions could look like on a real-data example
(Prophet auto-detected seasonality interval):