diff --git a/model_manager/sientia/models.py b/model_manager/sientia/models.py
index 4477297..f583736 100644
--- a/model_manager/sientia/models.py
+++ b/model_manager/sientia/models.py
@@ -535,8 +535,9 @@ class DataPreprocessor(BaseEstimator, TransformerMixin):
 
             # Normalization
             if step == NORMALIZATION and self.scaler:
-                data_treat = data_treat[self.feature_names_order]
-                data_treat[existing_columns] = self.scaler.transform(data_treat[existing_columns])
+                # Only transform feature columns, preserve target and any other required columns
+                feature_cols = self.feature_names_order
+                data_treat[feature_cols] = self.scaler.transform(data_treat[feature_cols])
 
             # Feature Creation
             if step == 'Feature Creation':
diff --git a/model_manager/utils/repository/training_repository.py b/model_manager/utils/repository/training_repository.py
index 07e4fa5..bf4115f 100644
--- a/model_manager/utils/repository/training_repository.py
+++ b/model_manager/utils/repository/training_repository.py
@@ -135,14 +135,28 @@ class TrainingRepository:
             scaler = tmr.process_data.get_scaler()
             self.logger.info('Denormalizing features')
 
-            for col in params.variable_columns:
-                tmr.x_train[col] = scaler.denormalize_single_input(tmr.x_train[col], col)
-                tmr.x_test[col] = scaler.denormalize_single_input(tmr.x_test[col], col)
+            # If using custom scaler with denormalize_* helpers
+            if hasattr(scaler, 'denormalize_single_input'):
+                for col in params.variable_columns:
+                    tmr.x_train[col] = scaler.denormalize_single_input(tmr.x_train[col], col)
+                    tmr.x_test[col] = scaler.denormalize_single_input(tmr.x_test[col], col)
 
-            self.logger.info('Denormalizing target variable')
-            tmr.y_train = scaler.denormalize_single_input(tmr.y_train, params.target_variable)
-            tmr.y_test = scaler.denormalize_single_input(tmr.y_test, params.target_variable)
-            y_pred_array = scaler.denormalize_predictions(y_pred_array, params.target_variable)
+                self.logger.info('Denormalizing target variable')
+                tmr.y_train = scaler.denormalize_single_input(tmr.y_train, params.target_variable)
+                tmr.y_test = scaler.denormalize_single_input(tmr.y_test, params.target_variable)
+                y_pred_array = scaler.denormalize_predictions(y_pred_array, params.target_variable)
+            else:
+                # Fallback for sklearn StandardScaler: only inverse-transform features
+                feature_cols = getattr(
+                    tmr.process_data, 'feature_names_order', params.variable_columns
+                )
+                # Ensure columns are in the same order used during fit
+                x_train_features = tmr.x_train[feature_cols]
+                x_test_features = tmr.x_test[feature_cols]
+
+                tmr.x_train[feature_cols] = scaler.inverse_transform(x_train_features)
+                tmr.x_test[feature_cols] = scaler.inverse_transform(x_test_features)
+                # Target was not scaled with StandardScaler in preprocessing; leave y as-is
 
         self.logger.info('Adding index to predictions')
         tmr.y_pred = pd.Series(y_pred_array, index=tmr.y_test.index)
@@ -237,5 +251,6 @@ class TrainingRepository:
             upp_lim=params.upp_lim,
             window=params.window,
             scaler_name='Standard Scaler' if params.use_scaler else 'None',
+            scaler_params={} if params.use_scaler else None,
             ar_var=params.target_variable if params.include_ar else None,
         )