diff --git a/docs/anomaly-detection/Overview.md b/docs/anomaly-detection/Overview.md
index d08940120..d164fa4c8 100644
--- a/docs/anomaly-detection/Overview.md
+++ b/docs/anomaly-detection/Overview.md
@@ -88,10 +88,6 @@ Currently, vmanomaly ships with a set of built-in models:
    See [statsmodels.org documentation](https://www.statsmodels.org/dev/examples/notebooks/generated/stl_decomposition.html)
    for LOESS STD.
 
-1. [**ARIMA**](/anomaly-detection/components/models.html#arima)
-
-   Commonly used forecasting model. See [statsmodels.org documentation](https://www.statsmodels.org/stable/generated/statsmodels.tsa.arima.model.ARIMA.html) for ARIMA.
-
 1. [**Rolling Quantile**](/anomaly-detection/components/models.html#rolling-quantile)
 
    A simple moving window of quantiles. Easy to use, easy to understand, but not as powerful as
diff --git a/docs/anomaly-detection/components/models.md b/docs/anomaly-detection/components/models.md
index e47d245e6..47e11626c 100644
--- a/docs/anomaly-detection/components/models.md
+++ b/docs/anomaly-detection/components/models.md
@@ -135,19 +135,19 @@ Introduced in [1.13.0](/anomaly-detection/CHANGELOG/#1130), `detection_direction
 
 Here's how default (backward-compatible) behavior looks like - anomalies will be tracked in `both` directions (`y > yhat` or `y < yhat`). This is useful when there is no domain expertise to filter the required direction.
 
-<img src="schema_detection_direction=both.webp" width="800px" alt="schema_detection_direction=both"/>
+<img src="/anomaly-detection/components/schema_detection_direction=both.webp" width="800px" alt="schema_detection_direction=both"/>
 
 When set to `above_expected`, anomalies are tracked only when `y > yhat`.
 
 *Example metrics*: Error rate, response time, page load time, number of failed transactions - metrics where *lower values are better*, so **higher** values are typically tracked.
 
-<img src="schema_detection_direction=above_expected.webp" width="800px" alt="schema_detection_direction=above_expected"/>
+<img src="/anomaly-detection/components/schema_detection_direction=above_expected.webp" width="800px" alt="schema_detection_direction=above_expected"/>
 
 When set to `below_expected`, anomalies are tracked only when `y < yhat`. 
 
 *Example metrics*: Service Level Agreement (SLA) compliance, conversion rate, Customer Satisfaction Score (CSAT) - metrics where *higher values are better*, so **lower** values are typically tracked.
 
-<img src="schema_detection_direction=below_expected.webp" width="800px" alt="schema_detection_direction=below_expected"/>
+<img src="/anomaly-detection/components/schema_detection_direction=below_expected.webp" width="800px" alt="schema_detection_direction=below_expected"/>
 
 Config with a split example:
 
@@ -193,11 +193,11 @@ Introduced in [v1.13.0](/anomaly-detection/CHANGELOG/#1130), the `min_dev_from_e
 
 Visualizations below demonstrate this concept; the green zone defined as the `[yhat - min_dev_from_expected, yhat + min_dev_from_expected]` range excludes actual data points (`y`) from generating anomaly scores if they fall within that range.
 
-<img src="schema_min_dev_from_expected=0.webp" width="800px" alt="min_dev_from_expected-default"/>
+<img src="/anomaly-detection/components/schema_min_dev_from_expected=0.webp" width="800px" alt="min_dev_from_expected-default"/>
 
-<img src="schema_min_dev_from_expected=1.0.webp" width="800px" alt="min_dev_from_expected-small"/>
+<img src="/anomaly-detection/components/schema_min_dev_from_expected=1.0.webp" width="800px" alt="min_dev_from_expected-small"/>
 
-<img src="schema_min_dev_from_expected=5.0.webp" width="800px" alt="min_dev_from_expected-big"/>
+<img src="/anomaly-detection/components/schema_min_dev_from_expected=5.0.webp" width="800px" alt="min_dev_from_expected-big"/>
 
 
 ## Model types
@@ -224,7 +224,7 @@ If during an inference, you got a series having **new labelset** (not present in
 **Examples:** [Prophet](#prophet), [Holt-Winters](#holt-winters)
 
 <p></p>
-<img alt="vmanomaly-model-type-univariate" src="model-lifecycle-univariate.webp" width="800px"/>
+<img alt="vmanomaly-model-type-univariate" src="/anomaly-detection/components/model-lifecycle-univariate.webp" width="800px"/>
 
 ### Multivariate Models
 
@@ -239,7 +239,7 @@ If during an inference, you got a **different amount of series** or some series
 **Examples:** [IsolationForest](#isolation-forest-multivariate)
 
 <p></p>
-<img alt="vmanomaly-model-type-multivariate" src="model-lifecycle-multivariate.webp" width="800px"/>
+<img alt="vmanomaly-model-type-multivariate" src="/anomaly-detection/components/model-lifecycle-multivariate.webp" width="800px"/>
 
 ### Rolling Models
 
@@ -256,7 +256,7 @@ Such models put **more pressure** on your reader's source, i.e. if your model sh
 **Examples:** [RollingQuantile](#rolling-quantile)
 
 <p></p>
-<img alt="vmanomaly-model-type-rolling" src="model-type-rolling.webp" width="800px"/>
+<img alt="vmanomaly-model-type-rolling" src="/anomaly-detection/components/model-type-rolling.webp" width="800px"/>
 
 ### Non-Rolling Models
 
@@ -273,7 +273,7 @@ Produced model instances are **stored in-memory** between consecutive re-fit cal
 **Examples:** [Prophet](#prophet)
 
 <p></p>
-<img alt="vmanomaly-model-type-non-rolling" src="model-type-non-rolling.webp" width="800px"/>
+<img alt="vmanomaly-model-type-non-rolling" src="/anomaly-detection/components/model-type-non-rolling.webp" width="800px"/>
 
 ## Built-in Models 
 
@@ -312,7 +312,7 @@ Tuning hyperparameters of a model can be tricky and often requires in-depth know
   - `n_trials` (int) - How many trials to sample from hyperparameter search space. The higher, the longer it takes but the better the results can be. Defaults to 128.
   - `timeout` (float) - How many seconds in total can be spent on each model to tune hyperparameters. The higher, the longer it takes, allowing to test more trials out of defined `n_trials`, but the better the results can be.
 
-<img alt="vmanomaly-autotune-schema" src="autotune.webp" width="800px"/>
+<img alt="vmanomaly-autotune-schema" src="/anomaly-detection/components/autotune.webp" width="800px"/>
 
 ```yaml
 # ...