From 7279899a8a24e423a3d454f168933e05aaeddce2 Mon Sep 17 00:00:00 2001 From: Daria Karavaieva Date: Sat, 17 Aug 2024 12:43:28 +0200 Subject: [PATCH] docs/vmanomaly: updated model list in Overview (#6832) ### 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/). --- docs/anomaly-detection/Overview.md | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/docs/anomaly-detection/Overview.md b/docs/anomaly-detection/Overview.md index c1b48605a..916ce4565 100644 --- a/docs/anomaly-detection/Overview.md +++ b/docs/anomaly-detection/Overview.md @@ -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):