diff --git a/src/autogluon/cloud/backend/timeseries_sagemaker_backend.py b/src/autogluon/cloud/backend/timeseries_sagemaker_backend.py
index 3bee739..e2efcb1 100644
--- a/src/autogluon/cloud/backend/timeseries_sagemaker_backend.py
+++ b/src/autogluon/cloud/backend/timeseries_sagemaker_backend.py
@@ -166,7 +166,7 @@ def predict_real_time(
         static_features: Optional[pd.DataFrame]
              An optional data frame describing the metadata attributes of individual items in the item index.
              For more detail, please refer to `TimeSeriesDataFrame` documentation:
-             https://auto.gluon.ai/stable/api/autogluon.predictor.html#timeseriesdataframe
+             https://auto.gluon.ai/stable/api/autogluon.timeseries.TimeSeriesDataFrame.html
         target: str
             Name of column that contains the target values to forecast
         accept: str, default = application/x-parquet
@@ -225,7 +225,7 @@ def predict(
         static_features: Optional[Union[str, pd.DataFrame]]
              An optional data frame describing the metadata attributes of individual items in the item index.
              For more detail, please refer to `TimeSeriesDataFrame` documentation:
-             https://auto.gluon.ai/stable/api/autogluon.predictor.html#timeseriesdataframe
+             https://auto.gluon.ai/stable/api/autogluon.timeseries.TimeSeriesDataFrame.html
         target: str
             Name of column that contains the target values to forecast
         kwargs:
diff --git a/src/autogluon/cloud/predictor/timeseries_cloud_predictor.py b/src/autogluon/cloud/predictor/timeseries_cloud_predictor.py
index 8216957..97cb04c 100644
--- a/src/autogluon/cloud/predictor/timeseries_cloud_predictor.py
+++ b/src/autogluon/cloud/predictor/timeseries_cloud_predictor.py
@@ -80,7 +80,7 @@ def fit(
         static_features: Optional[pd.DataFrame]
              An optional data frame describing the metadata attributes of individual items in the item index.
              For more detail, please refer to `TimeSeriesDataFrame` documentation:
-             https://auto.gluon.ai/stable/api/autogluon.predictor.html#timeseriesdataframe
+             https://auto.gluon.ai/stable/api/autogluon.timeseries.TimeSeriesDataFrame.html
         framework_version: str, default = `latest`
             Training container version of autogluon.
             If `latest`, will use the latest available container version.
@@ -159,6 +159,7 @@ def predict_real_time(
         self,
         test_data: Union[str, pd.DataFrame],
         static_features: Optional[Union[str, pd.DataFrame]] = None,
+        known_covariates: Optional[pd.DataFrame] = None,
         accept: str = "application/x-parquet",
         **kwargs,
     ) -> pd.DataFrame:
@@ -175,7 +176,12 @@ def predict_real_time(
         static_features: Optional[pd.DataFrame]
              An optional data frame describing the metadata attributes of individual items in the item index.
              For more detail, please refer to `TimeSeriesDataFrame` documentation:
-             https://auto.gluon.ai/stable/api/autogluon.predictor.html#timeseriesdataframe
+             https://auto.gluon.ai/stable/api/autogluon.timeseries.TimeSeriesDataFrame.html
+        known_covariates : Optional[pd.DataFrame]
+            If ``known_covariates_names`` were specified when creating the predictor, it is necessary to provide the
+            values of the known covariates for each time series during the forecast horizon.
+            For more details, please refer to the `TimeSeriesPredictor.predictor` documentation:
+            https://auto.gluon.ai/stable/api/autogluon.timeseries.TimeSeriesPredictor.predict.html
         accept: str, default = application/x-parquet
             Type of accept output content.
             Valid options are application/x-parquet, text/csv, application/json
@@ -198,6 +204,7 @@ def predict_real_time(
             target=self.target_column,
             static_features=static_features,
             accept=accept,
+            inference_kwargs=dict(known_covariates=known_covariates, **kwargs),
         )
 
     def predict_proba_real_time(self, **kwargs) -> pd.DataFrame:
@@ -224,6 +231,9 @@ def predict(
         This method would first create a AutoGluonSagemakerInferenceModel with the trained predictor,
         then create a transformer with it, and call transform in the end.
 
+        Note that batch prediction with `known_covariates` is currently not supported.  Please use `predict_real_time`
+        to predict with `known_covariates` instead.
+
         Parameters
         ----------
         test_data: str
@@ -232,7 +242,7 @@ def predict(
         static_features: Optional[Union[str, pd.DataFrame]]
              An optional data frame describing the metadata attributes of individual items in the item index.
              For more detail, please refer to `TimeSeriesDataFrame` documentation:
-             https://auto.gluon.ai/stable/api/autogluon.predictor.html#timeseriesdataframe
+             https://auto.gluon.ai/stable/api/autogluon.timeseries.TimeSeriesDataFrame.html
         target: str
             Name of column that contains the target values to forecast
         predictor_path: str