diff --git a/bigframes/ml/forecasting.py b/bigframes/ml/forecasting.py
index 6079e0ea22..9dc1652912 100644
--- a/bigframes/ml/forecasting.py
+++ b/bigframes/ml/forecasting.py
@@ -199,6 +199,8 @@ def _fit(
         Returns:
             ARIMAPlus: Fitted estimator.
         """
+        X, y = utils.batch_convert_to_dataframe(X, y)
+
         if X.columns.size != 1:
             raise ValueError(
                 "Time series timestamp input X must only contain 1 column."
@@ -206,8 +208,6 @@ def _fit(
         if y.columns.size != 1:
             raise ValueError("Time series data input y must only contain 1 column.")
 
-        X, y = utils.batch_convert_to_dataframe(X, y)
-
         self._bqml_model = self._bqml_model_factory.create_time_series_model(
             X,
             y,
diff --git a/tests/system/large/ml/test_forecasting.py b/tests/system/large/ml/test_forecasting.py
index 438177b1a0..bb53305b94 100644
--- a/tests/system/large/ml/test_forecasting.py
+++ b/tests/system/large/ml/test_forecasting.py
@@ -36,7 +36,7 @@
 @pytest.fixture(scope="module")
 def arima_model(time_series_df_default_index):
     model = forecasting.ARIMAPlus()
-    X_train = time_series_df_default_index[["parsed_date"]]
+    X_train = time_series_df_default_index["parsed_date"]
     y_train = time_series_df_default_index[["total_visits"]]
     model.fit(X_train, y_train)
     return model
@@ -114,7 +114,7 @@ def test_arima_plus_model_fit_params(time_series_df_default_index, dataset_id):
     )
 
     X_train = time_series_df_default_index[["parsed_date"]]
-    y_train = time_series_df_default_index[["total_visits"]]
+    y_train = time_series_df_default_index["total_visits"]
     model.fit(X_train, y_train)
 
     # save, load to ensure configuration was kept