diff --git a/morpheus/stages/postprocess/timeseries_stage.py b/morpheus/stages/postprocess/timeseries_stage.py
index 2a7b16f945..274032d871 100644
--- a/morpheus/stages/postprocess/timeseries_stage.py
+++ b/morpheus/stages/postprocess/timeseries_stage.py
@@ -367,7 +367,7 @@ def _calc_timeseries(self, x: MultiResponseMessage | ControlMessage, is_complete
             if isinstance(x, MultiResponseMessage):
                 new_timedata = x.get_meta([self._timestamp_col])
             elif isinstance(x, ControlMessage):
-                new_timedata = x.payload().get_data([self._timestamp_col])
+                new_timedata = x.payload().get_data([self._timestamp_col]).to_pandas()
 
             # Save this message event times in the event list. Ensure the values are always sorted
             self._timeseries_data = pd.concat([self._timeseries_data, new_timedata]).sort_index()
diff --git a/tests/stages/test_timeseries_stage.py b/tests/stages/test_timeseries_stage.py
index 893489dba3..d399bfeca6 100644
--- a/tests/stages/test_timeseries_stage.py
+++ b/tests/stages/test_timeseries_stage.py
@@ -72,8 +72,5 @@ def test_call_timeseries_user(config):
     df = pd.DataFrame({"ts": pd.date_range(start='01-01-2022', periods=5)})
     probs = cp.array([[0.1, 0.5, 0.3], [0.2, 0.3, 0.4]])
     mock_multi_response_ae_message = _make_multi_response_ae_message(df, probs)
-    mock_control_message = _make_control_message(df, probs)
 
-    print("test")
-    print(stage._call_timeseries_user(mock_multi_response_ae_message))
-    print(stage._call_timeseries_user(mock_control_message))
+    assert stage._call_timeseries_user(mock_multi_response_ae_message)[0].user_id == "test_user_id"
diff --git a/tests/test_filter_detections_stage_pipe.py b/tests/test_filter_detections_stage_pipe.py
index a5ee68d7d5..e90ea13b3f 100755
--- a/tests/test_filter_detections_stage_pipe.py
+++ b/tests/test_filter_detections_stage_pipe.py
@@ -56,8 +56,6 @@ def _test_filter_detections_stage_pipe(config: Config,
         input_df = dataset_pandas.repeat(input_df, repeat_count=repeat)
 
     threshold = 0.75
-    print("expected1")
-    print(build_expected(dataset_pandas["filter_probs.csv"], threshold))
 
     pipe = LinearPipeline(config)
     pipe.set_source(InMemorySourceStage(config, [cudf.DataFrame(input_df)]))