docs/vmanomaly: patch release 1.16.1 (#7169)

### Describe Your Changes

`vmanomaly` patch release 1.16.1 updates

### Checklist

The following checks are **mandatory**:

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

deployment/docker/vmanomaly/vmanomaly-integration/docker-compose.yml

@@ -73,7 +73,7 @@ services:
     restart: always
   vmanomaly:
     container_name: vmanomaly
-    image: victoriametrics/vmanomaly:v1.16.0
+    image: victoriametrics/vmanomaly:v1.16.1
     depends_on:
       - "victoriametrics"
     ports:

docs/anomaly-detection/CHANGELOG.md

@@ -11,8 +11,15 @@ aliases:
 ---
 Please find the changelog for VictoriaMetrics Anomaly Detection below.
 
-### v1.16.0
+## v1.16.1
+Released: 2024-10-02
+- FIX: This patch release prevents the service from crashing by rolling back the version of a third-party dependency. Affected releases: [v1.16.0](#v1160).
+
+## v1.16.0
 Released: 2024-10-01
+
+> **Note**: A bug was discovered in this release that causes the service to crash. Please use the patch [v1.16.1](#v1161) to resolve this issue.
+
 - FEATURE: Introduced data dumps to a host filesystem for [VmReader](https://docs.victoriametrics.com/anomaly-detection/components/reader#vm-reader).  Resource-intensive setups (multiple queries returning many metrics, bigger `fit_window` arg) will have RAM consumption reduced during fit calls.
 - IMPROVEMENT: Added a `groupby` argument for logical grouping in [multivariate models](https://docs.victoriametrics.com/anomaly-detection/components/models#multivariate-models). When specified, a separate multivariate model is trained for each unique combination of label values in the `groupby` columns. For example, to perform multivariate anomaly detection on metrics at the machine level without cross-entity interference, you can use `groupby: [host]` or `groupby: [instance]`, ensuring one model per entity being trained (e.g., per host). Please find more details [here](https://docs.victoriametrics.com/anomaly-detection/components/models/#group-by).
 - IMPROVEMENT: Improved performance of [VmReader](https://docs.victoriametrics.com/anomaly-detection/components/reader#vm-reader) on multicore instances for reading and data processing.

docs/anomaly-detection/components/models.md

@@ -961,7 +961,7 @@ monitoring:
 Let's pull the docker image for `vmanomaly`:
 
 ```sh
-docker pull victoriametrics/vmanomaly:v1.16.0
+docker pull victoriametrics/vmanomaly:v1.16.1
 ```
 
 Now we can run the docker container putting as volumes both config and model file:
@@ -975,7 +975,7 @@ docker run -it \
 -v $(PWD)/license:/license \
 -v $(PWD)/custom_model.py:/vmanomaly/model/custom.py \
 -v $(PWD)/custom.yaml:/config.yaml \
-victoriametrics/vmanomaly:v1.16.0 /config.yaml \
+victoriametrics/vmanomaly:v1.16.1 /config.yaml \
 --licenseFile=/license
 ```
 

docs/anomaly-detection/guides/guide-vmanomaly-vmalert/README.md

@@ -385,7 +385,7 @@ services:
     restart: always
   vmanomaly:
     container_name: vmanomaly
-    image: victoriametrics/vmanomaly:v1.16.0
+    image: victoriametrics/vmanomaly:v1.16.1
     depends_on:
       - "victoriametrics"
     ports: