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Signed-off-by: Artem Navoiev <tenmozes@gmail.com>
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docs/anomaly-detection/components/models.md
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@ -249,7 +249,6 @@ If during an inference, you got a series having **new labelset** (not present in
**Examples:** [Prophet](#prophet), [Holt-Winters](#holt-winters)
<p></p>
![vmanomaly-model-type-univariate](model-lifecycle-univariate.webp)
@ -265,7 +264,6 @@ If during an inference, you got a **different amount of series** or some series
**Examples:** [IsolationForest](#isolation-forest-multivariate)
<p></p>
![vmanomaly-model-type-multivariate](model-lifecycle-multivariate.webp)
@ -283,7 +281,6 @@ Such models put **more pressure** on your reader's source, i.e. if your model sh
**Examples:** [RollingQuantile](#rolling-quantile)
<p></p>
![vmanomaly-model-type-rolling](model-type-rolling.webp)
@ -301,9 +298,8 @@ Produced model instances are **stored in-memory** between consecutive re-fit cal
**Examples:** [Prophet](#prophet)
<p></p>
![vmanomaly-model-type-non-rolling](model-type-non-rolling.webp)
{width="800px}
## Built-in Models