diff --git a/docs/source/overview/getting-started/installation-options.rst b/docs/source/overview/getting-started/installation-options.rst
index 7ab700be3..95053ebcf 100644
--- a/docs/source/overview/getting-started/installation-options.rst
+++ b/docs/source/overview/getting-started/installation-options.rst
@@ -13,5 +13,7 @@ EvaDB provides the following additional installation options for extending its f
 
 * ``pip install evadb[ludwig]`` for installing the Ludwig model training framework. It enables use-cases related to training and fine-tuning AI models.
 
+* ``pip install evadb[forecasting]`` for installing the statsforecast forecasting framework. It enables use-cases related to time series forecasting.
+
 * ``pip install evadb[ray]`` for installing the Ray compute engine. It enables EvaDB to do more efficient query execution on CPUs and GPUs.
 
diff --git a/docs/source/reference/ai/model-forecasting.rst b/docs/source/reference/ai/model-forecasting.rst
index 75602a035..115c268d4 100644
--- a/docs/source/reference/ai/model-forecasting.rst
+++ b/docs/source/reference/ai/model-forecasting.rst
@@ -19,11 +19,11 @@ First, we create a table to insert required data.
    LOAD CSV 'data/forecasting/air-passengers.csv' INTO AirData;
 
 
-Next, we create a UDF of `TYPE Forecasting`. We must enter the column name on which we wish to forecast using `predict`. Other options include `id` and `time` (they represent the unique id of the items and the time data if available).
+Next, we create a function of `TYPE Forecasting`. We must enter the column name on which we wish to forecast using `predict`. Other options include `id` and `time` (they represent the unique id of the items and the time data if available).
 
 .. code-block:: sql
    
-   CREATE UDF IF NOT EXISTS Forecast FROM
+   CREATE FUNCTION IF NOT EXISTS Forecast FROM
    (SELECT y FROM AirData)
    TYPE Forecasting
    PREDICT 'y';