From e5beeb18d38d26501c3535998f583d228adf2b9d Mon Sep 17 00:00:00 2001 From: Fred Navruzov Date: Wed, 17 Jul 2024 18:52:05 +0200 Subject: [PATCH] docs/vmanomaly - v1.13.3 patch notes (#6659) ### Describe Your Changes Add patch note v1.13.3 to CHANGELOG doc page for `vmanomaly` ### Checklist The following checks are **mandatory**: - [x] My change adheres [VictoriaMetrics contributing guidelines](https://docs.victoriametrics.com/contributing/). --- docs/anomaly-detection/CHANGELOG.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/docs/anomaly-detection/CHANGELOG.md b/docs/anomaly-detection/CHANGELOG.md index 73e119ac8..1bf25db6b 100644 --- a/docs/anomaly-detection/CHANGELOG.md +++ b/docs/anomaly-detection/CHANGELOG.md @@ -15,7 +15,11 @@ aliases: Please find the changelog for VictoriaMetrics Anomaly Detection below. -> **Important note: Users are strongly encouraged to upgrade to `vmanomaly` [v1.9.2](https://hub.docker.com/repository/docker/victoriametrics/vmanomaly/tags?page=1&ordering=name) or newer for optimal performance and accuracy.

This recommendation is crucial for configurations with a low `infer_every` parameter [in your scheduler](https://docs.victoriametrics.com/anomaly-detection/components/scheduler/#parameters-1), and in scenarios where data exhibits significant high-order seasonality patterns (such as hourly or daily cycles). Previous versions from v1.5.1 to v1.8.0 were identified to contain a critical issue impacting model training, where models were inadvertently trained on limited data subsets, leading to suboptimal fits, affecting the accuracy of anomaly detection.

Upgrading to v1.9.2 addresses this issue, ensuring proper model training and enhanced reliability. For users utilizing Helm charts, it is recommended to upgrade to version [1.0.0](https://github.com/VictoriaMetrics/helm-charts/blob/master/charts/victoria-metrics-anomaly/CHANGELOG.md#100) or newer.** +> **Important note: Users are strongly encouraged to upgrade to `vmanomaly` [v1.9.2](https://hub.docker.com/repository/docker/victoriametrics/vmanomaly/tags?page=1&ordering=name) or newer for optimal performance and accuracy.

This recommendation is crucial for configurations with a low `infer_every` parameter [in your scheduler](/anomaly-detection/components/scheduler/#parameters-1), and in scenarios where data exhibits significant high-order seasonality patterns (such as hourly or daily cycles). Previous versions from v1.5.1 to v1.8.0 were identified to contain a critical issue impacting model training, where models were inadvertently trained on limited data subsets, leading to suboptimal fits, affecting the accuracy of anomaly detection.

Upgrading to v1.9.2 addresses this issue, ensuring proper model training and enhanced reliability. For users utilizing Helm charts, it is recommended to upgrade to version [1.0.0](https://github.com/VictoriaMetrics/helm-charts/blob/master/charts/victoria-metrics-anomaly/CHANGELOG.md#100) or newer.** + +## v1.13.3 +Released: 2024-07-17 +- FIX: now validation of `args` argument for [`HoltWinters`](/anomaly-detection/components/models#holt-winters) model works properly. ## v1.13.2 Released: 2024-07-15