diff --git a/neuralprophet/forecaster.py b/neuralprophet/forecaster.py
index ae5ea6e05..e2c34f463 100644
--- a/neuralprophet/forecaster.py
+++ b/neuralprophet/forecaster.py
@@ -1221,8 +1221,8 @@ def fit(
             # df_val, _, _, _ = df_utils.prep_or_copy_df(df_val)
             df_val = _normalize(df=df_val, config_normalization=self.config_normalization)
             val_components_stacker = utils_time_dataset.ComponentStacker(
-                n_lags=self.n_lags,
-                max_lags=self.max_lags,
+                n_lags=self.config_ar.n_lags,
+                max_lags=self.config_model.max_lags,
                 n_forecasts=self.n_forecasts,
                 config_seasonality=self.config_seasonality,
                 lagged_regressor_config=self.config_lagged_regressors,
@@ -1461,9 +1461,9 @@ def test(self, df: pd.DataFrame, verbose: bool = True):
         df, _, _, _ = df_utils.prep_or_copy_df(df)
         df = _normalize(df=df, config_normalization=self.config_normalization)
         components_stacker = utils_time_dataset.ComponentStacker(
-            n_lags=self.n_lags,
+            n_lags=self.config_ar.n_lags,
             n_forecasts=self.n_forecasts,
-            max_lags=self.max_lags,
+            max_lags=self.config_model.max_lags,
             config_seasonality=self.config_seasonality,
             lagged_regressor_config=self.config_lagged_regressors,
             feature_indices={},
@@ -2116,7 +2116,7 @@ def predict_seasonal_components(self, df: pd.DataFrame, quantile: float = 0.5):
                 max_lags=0,
                 n_forecasts=1,
                 config_seasonality=self.config_seasonality,
-                lagged_regressor_config=self.config_lagged_regressors,
+                lagged_regressor_config=None,
             )
             dataset = time_dataset.TimeDataset(
                 df=df_i,
@@ -2959,9 +2959,9 @@ def _predict_raw(self, df, df_name, include_components=False, prediction_frequen
         if "y_scaled" not in df.columns or "t" not in df.columns:
             raise ValueError("Received unprepared dataframe to predict. " "Please call predict_dataframe_to_predict.")
         components_stacker = utils_time_dataset.ComponentStacker(
-            n_lags=self.n_lags,
+            n_lags=self.config_ar.n_lags,
             n_forecasts=self.n_forecasts,
-            max_lags=self.max_lags,
+            max_lags=self.config_model.max_lags,
             config_seasonality=self.config_seasonality,
             lagged_regressor_config=self.config_lagged_regressors,
             feature_indices={},
diff --git a/neuralprophet/utils_time_dataset.py b/neuralprophet/utils_time_dataset.py
index 323d99a4f..7a694196c 100644
--- a/neuralprophet/utils_time_dataset.py
+++ b/neuralprophet/utils_time_dataset.py
@@ -1,5 +1,7 @@
 from collections import OrderedDict
+from datetime import datetime
 
+import numpy as np
 import torch