diff --git a/tests/toolkit/test_time_series_forecasting_pipeline.py b/tests/toolkit/test_time_series_forecasting_pipeline.py
index c22b1cca..defdd123 100644
--- a/tests/toolkit/test_time_series_forecasting_pipeline.py
+++ b/tests/toolkit/test_time_series_forecasting_pipeline.py
@@ -120,30 +120,13 @@ def test_forecasting_pipeline_forecasts_with_preprocessor():
     model = PatchTSTForPrediction.from_pretrained(model_path)
     context_length = model.config.context_length
 
-    tsp = TimeSeriesPreprocessor(
-        timestamp_column=timestamp_column,
-        id_columns=id_columns,
-        target_columns=target_columns,
-        context_length=context_length,
-        prediction_length=prediction_length,
-        freq="1h",
-    )
-
-    forecast_pipeline = TimeSeriesForecastingPipeline(
-        model=model,
-        timestamp_column=timestamp_column,
-        id_columns=id_columns,
-        target_columns=target_columns,
-        freq="1h",
-        feature_extractor=tsp,
-        explode_forecasts=False,
-    )
-
     dataset_path = "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTh2.csv"
     data = pd.read_csv(
         dataset_path,
         parse_dates=[timestamp_column],
     )
+    train_end_index = 12 * 30 * 24
+
     test_end_index = 12 * 30 * 24 + 8 * 30 * 24
     test_start_index = test_end_index - context_length - 4
 
@@ -152,6 +135,12 @@ def test_forecasting_pipeline_forecasts_with_preprocessor():
         parse_dates=[timestamp_column],
     )
 
+    train_data = select_by_index(
+        data,
+        id_columns=id_columns,
+        start_index=0,
+        end_index=train_end_index,
+    )
     test_data = select_by_index(
         data,
         id_columns=id_columns,
@@ -159,11 +148,35 @@ def test_forecasting_pipeline_forecasts_with_preprocessor():
         end_index=test_end_index,
     )
 
-    forecasts = forecast_pipeline(test_data)
+    tsp = TimeSeriesPreprocessor(
+        timestamp_column=timestamp_column,
+        id_columns=id_columns,
+        target_columns=target_columns,
+        context_length=context_length,
+        prediction_length=prediction_length,
+        freq="1h",
+        scaling=True,
+    )
+
+    tsp.train(train_data)
+
+    forecast_pipeline = TimeSeriesForecastingPipeline(
+        model=model,
+        timestamp_column=timestamp_column,
+        id_columns=id_columns,
+        target_columns=target_columns,
+        freq="1h",
+        feature_extractor=tsp,
+        explode_forecasts=False,
+        inverse_scale_outputs=True,
+    )
+
+    forecasts = forecast_pipeline(tsp.preprocess(test_data))
 
     assert forecasts.shape == (
         test_end_index - test_start_index - context_length + 1,
         2 * len(target_columns) + 1,
     )
 
-    # to do: add check on the scaling
+    # if we have inverse scaled mean should be larger
+    assert forecasts["HUFL_prediction"].mean().mean() > 10