From fbbd5ab1e72b14b91df55a11320e3767711b7a1b Mon Sep 17 00:00:00 2001 From: Artem Navoiev Date: Sun, 21 Jan 2024 22:21:37 +0100 Subject: [PATCH] docs vmanomaly fix anchor Signed-off-by: Artem Navoiev --- docs/anomaly-detection/components/models.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/anomaly-detection/components/models.md b/docs/anomaly-detection/components/models.md index 0bde452789..a9ca848632 100644 --- a/docs/anomaly-detection/components/models.md +++ b/docs/anomaly-detection/components/models.md @@ -41,7 +41,7 @@ VM Anomaly Detection (`vmanomaly` hereinafter) models support 2 groups of parame * [Seasonal Trend Decomposition](#seasonal-trend-decomposition) - similarly to Holt-Winters, is best for **data with pronounced [seasonal](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#seasonality) and [trend](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#trend) components** * [ARIMA](#arima) - use when your data shows **clear patterns or autocorrelation (the degree of correlation between values of the same series at different periods)**. However, good understanding of machine learning is required to tune. * [Isolation forest (Multivariate)](#isolation-forest-multivariate) - useful for **metrics data interaction** (several queries/metrics -> single anomaly score) and **efficient in detecting anomalies in high-dimensional datasets** -* [Custom model](#custom-model) - benefit from your own models and expertise to better support your **unique use case**. +* [Custom model](#custom-model-guide) - benefit from your own models and expertise to better support your **unique use case**. ### [Prophet](https://facebook.github.io/prophet/)