[DOCS] Update release notes for IEI Time Series for main (open-edge-platform#2305)

Signed-off-by: Sebastian Golebiewski <sebastianx.golebiewski@intel.com>

Author: sgolebiewski-intel

manufacturing-ai-suite/industrial-edge-insights-time-series/docs/user-guide/index.md

@@ -46,7 +46,7 @@ how-to-guides
 weld-anomaly-detection/index
 wind-turbine-anomaly-detection/index
 troubleshooting
-release-notes
+Release Notes <release-notes>
 
 :::
 hide_directive-->

manufacturing-ai-suite/industrial-edge-insights-time-series/docs/user-guide/release-notes.md

@@ -1,33 +1,34 @@
-# Industrial Edge Insights - Time Series Release Notes
+# Release Notes: Industrial Edge Insights - Time Series
 
-## v2025.2 - December 2025
+## Version 2026.0
 
-This release introduces substantial enhancements to the Time Series AI stack, including a new sample application and several key features detailed below.
+**March 2026**
 
-### Time Series AI Stack Enhancements
+This release introduces **new documentation for UDF development and wind turbine
+model integration**, along with **updated service images** and
+**documentation improvements**.
 
-- Introduced Makefile support for scalable processing of multiple input streams via OPC-UA and MQTT protocols, enabling effective benchmarking of sample applications.
-- Updated Makefile to support multiple sample applications through an app parameter.
-- Enabled GPU-based inferencing for both Docker Compose and Helm deployments.
-- Removed model registry microservice code and documentation from sample applications.
-- Integrated nginx reverse proxy to centralize external traffic for web applications and REST API servers, reducing port exposure.
-- Refactored configuration files, codebase, and documentation to eliminate redundancy.
-- Added documentation for secure connectivity to internal and external MQTT brokers.
-- Implemented various improvements in documentation, usability, and configuration management for both Docker Compose and Helm deployments.
+**New**
 
-### Wind Turbine Anomaly Detection - v1.1.0
+- **UDF Development Guide** — Added a comprehensive guide for writing
+  User Defined Functions (UDFs) covering architecture, implementation steps, and
+  examples.
+- **Wind Turbine Model Guidelines** — Added wind turbine model selection and
+  integration guidelines.
 
-- Enabled iGPU based inferencing for the machine learning model using the scikit-learn-intelex package.
+**Improved**
 
-### Weld Anomaly Detection - v1.0.0
-
-- Introduced a weld anomaly detection sample application featuring dataset ingestion, CatBoost machine learning model integration, and a dedicated Grafana dashboard.
+- Updated image tags/versioning and aligned deployment/documentation examples.
+- Updated third-party service image versions used by Time Series (Telegraf,
+  Grafana, Eclipse Mosquitto).
+- Reorganized Time Series how-to guides for better navigation.
+- Updated Time Series documentation toctree structure.
+- Fixed failing code blocks in Time Series documentation.
+- Fixed formatting issues in Wind Turbine Anomaly documentation.
 
 <!--hide_directive
-:::{toctree}
+```{toctree}
 :hidden:
-
-release-notes/aug-2025
-
-:::
+Release Notes 2025 <./release-notes/release-notes-2025>
+```
 hide_directive-->

manufacturing-ai-suite/industrial-edge-insights-time-series/docs/user-guide/release-notes/aug-2025.md

@@ -0,0 +1,60 @@
+# Release Notes: Industrial Edge Insights - Time Series 2025
+
+## Version 2025.2
+
+**December 2025**
+
+This release introduces substantial enhancements to the Time Series AI stack,
+including a new sample application and several key features detailed below.
+
+**New**
+
+- Introduced Makefile support for scalable processing of multiple input
+  streams via OPC-UA and MQTT protocols, enabling effective benchmarking of
+  sample applications.
+- Updated Makefile to support multiple sample applications through an app parameter.
+- Enabled GPU-based inferencing for both Docker Compose and Helm deployments.
+- Integrated nginx reverse proxy to centralize external traffic for web applications
+  and REST API servers, reducing port exposure.
+- Added documentation for secure connectivity to internal and external MQTT brokers.
+- Introduced Weld Anomaly Detection (v1.0.0) sample application featuring dataset
+  ingestion, CatBoost machine learning model integration, and a dedicated
+  Grafana dashboard.
+- Wind Turbine Anomaly Detection - v1.1.0:
+
+  - Enabled iGPU based inferencing for the machine learning model using the
+    scikit-learn-intelex package.
+
+**Improved**
+
+- Refactored configuration files, codebase, and documentation to eliminate redundancy.
+- Implemented various improvements in documentation, usability, and configuration
+  management for both Docker Compose and Helm deployments.
+- Removed model registry microservice code and documentation from sample applications.
+
+## Version 1.0.0
+
+**August 2025**
+
+This is
+[the first version](https://github.com/open-edge-platform/edge-ai-suites/commit/cba19ac887b61dd370e563aedb205a8458cf0eea)
+of the Wind Turbine Anomaly detection sample app showcasing a time series use
+case by detecting the anomalous power generation patterns relative to wind speed.
+
+**New**
+
+- Docker compose deployment on single node.
+- Helm deployment on Kubernetes single cluster node.
+- Added sample OPC-UA server and MQTT publisher data simulators to ingest the
+  wind turbine data.
+- Generic Time Series AI stack supporting the data ingestion, data analytics,
+  data storage and data visualization.
+- Data Analytics is powered by
+  [Time Series Analytics Microservice](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/time-series-analytics/index.html)
+  which from the sample app context takes in the configuration related to wind
+  turbine sample app and the User Defined Function(UDF) deployment package and
+  provides below capabilities:
+  - Provides the OPC-UA connector to publish the anomaly alerts to configured
+    OPC-UA server.
+  - Provides support to publish the anomaly alerts to configured MQTT server.
+  - Provides support to customize the UDF deployment package.