diff --git a/test/test_df_utils.py b/test/test_df_utils.py
index 6f0438e..dfa79ea 100644
--- a/test/test_df_utils.py
+++ b/test/test_df_utils.py
@@ -409,3 +409,48 @@ def test_convert_df_with_freq_and_validate_inputs_false(use_future_df):
     assert len(prediction_timestamps) == 2
     for series_id in ["A", "B"]:
         assert len(prediction_timestamps[series_id]) == prediction_length
+
+
+@pytest.mark.parametrize("use_future_df", [True, False])
+def test_convert_df_with_mismatched_freq_uses_user_provided_freq(use_future_df):
+    """Test that user-provided freq overrides data frequency when validate_inputs=False."""
+    # Create data with hourly frequency
+    data_freq = "h"
+    df = create_df(series_ids=["A", "B"], n_points=[10, 12], target_cols=["target"], covariates=["cov1"], freq=data_freq)
+    prediction_length = 5
+
+    # User provides daily frequency (different from data)
+    user_freq = "D"
+
+    future_df = None
+    if use_future_df:
+        # Create future_df with hourly frequency (matching data, not user freq)
+        forecast_start_times = get_forecast_start_times(df, freq=data_freq)
+        future_df = create_future_df(
+            forecast_start_times=forecast_start_times,
+            series_ids=["A", "B"],
+            n_points=[prediction_length, prediction_length],
+            covariates=["cov1"],
+            freq=data_freq
+        )
+
+    inputs, original_order, prediction_timestamps = convert_df_input_to_list_of_dicts_input(
+        df=df,
+        future_df=future_df,
+        target_columns=["target"],
+        prediction_length=prediction_length,
+        freq=user_freq,
+        validate_inputs=False,
+    )
+
+    # Prediction should work
+    assert len(inputs) == 2
+    assert len(prediction_timestamps) == 2
+
+    # Forecast timestamps should use user-provided freq (daily), not data freq (hourly)
+    for series_id in ["A", "B"]:
+        pred_ts = prediction_timestamps[series_id]
+        assert len(pred_ts) == prediction_length
+        # Verify the frequency matches user-provided freq
+        inferred_freq = pd.infer_freq(pred_ts)
+        assert inferred_freq == user_freq