diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py
index fc352ea8b..c393420b5 100644
--- a/freqtrade/freqai/freqai_interface.py
+++ b/freqtrade/freqai/freqai_interface.py
@@ -65,7 +65,6 @@ class IFreqaiModel(ABC):
         self.data_split_parameters = config.get("freqai", {}).get("data_split_parameters")
         self.model_training_parameters = config.get("freqai", {}).get("model_training_parameters")
         self.feature_parameters = config.get("freqai", {}).get("feature_parameters")
-        self.model = None
         self.retrain = False
         self.first = True
         self.set_full_path()
@@ -372,8 +371,8 @@ class IFreqaiModel(ABC):
         """
         Base data cleaning method for train
         Any function inside this method should drop training data points from the filtered_dataframe
-        based on user decided logic. See FreqaiDataKitchen::remove_outliers() for an example
-        of how outlier data points are dropped from the dataframe used for training.
+        based on user decided logic. See FreqaiDataKitchen::use_SVM_to_remove_outliers() for an
+        example of how outlier data points are dropped from the dataframe used for training.
         """
 
         if self.freqai_info.get("feature_parameters", {}).get(
diff --git a/freqtrade/freqai/prediction_models/BaseRegressionModel.py b/freqtrade/freqai/prediction_models/BaseRegressionModel.py
index c2fd53d0f..2db025fd6 100644
--- a/freqtrade/freqai/prediction_models/BaseRegressionModel.py
+++ b/freqtrade/freqai/prediction_models/BaseRegressionModel.py
@@ -1,6 +1,7 @@
 import logging
-from typing import Tuple
+from typing import Any, Tuple
 
+import numpy.typing as npt
 from pandas import DataFrame
 
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
@@ -28,7 +29,7 @@ class BaseRegressionModel(IFreqaiModel):
 
     def train(
         self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
-    ) -> Tuple[DataFrame, DataFrame]:
+    ) -> Any:
         """
         Filter the training data and train a model to it. Train makes heavy use of the datakitchen
         for storing, saving, loading, and analyzing the data.
@@ -83,7 +84,7 @@ class BaseRegressionModel(IFreqaiModel):
 
     def predict(
         self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
-    ) -> Tuple[DataFrame, DataFrame]:
+    ) -> Tuple[DataFrame, npt.ArrayLike]:
         """
         Filter the prediction features data and predict with it.
         :param: unfiltered_dataframe: Full dataframe for the current backtest period.
diff --git a/tests/freqai/test_freqai_interface.py b/tests/freqai/test_freqai_interface.py
index ce1a52bbf..6699ef563 100644
--- a/tests/freqai/test_freqai_interface.py
+++ b/tests/freqai/test_freqai_interface.py
@@ -194,3 +194,30 @@ def test_follow_mode(mocker, freqai_conf):
     assert len(freqai.dk.return_dataframe.index) == 5702
 
     shutil.rmtree(Path(freqai.dk.full_path))
+
+
+def test_principal_component_analysis(mocker, freqai_conf):
+    freqai_conf.update({"timerange": "20180110-20180130"})
+    freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
+        {"princpial_component_analysis": "true"})
+
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.live = True
+    freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
+    timerange = TimeRange.parse_timerange("20180110-20180130")
+    freqai.dk.load_all_pair_histories(timerange)
+
+    freqai.dd.pair_dict = MagicMock()
+
+    data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
+    new_timerange = TimeRange.parse_timerange("20180120-20180130")
+
+    freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
+
+    assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_pca_object.pkl")
+
+    shutil.rmtree(Path(freqai.dk.full_path))