diff --git a/src/delta_barth/analysis/forecast.py b/src/delta_barth/analysis/forecast.py
index 7b9d09e..0de4843 100644
--- a/src/delta_barth/analysis/forecast.py
+++ b/src/delta_barth/analysis/forecast.py
@@ -254,32 +254,13 @@ def _process_sales(
     # Option A: pad data frame with zero values --> could impede forecast algorithm
     # Option B: calculate next index based on timedelta
     stride = dopt_basics.datetime.timedelta_from_val(365, TimeUnitsTimedelta.DAYS)
-
     dates = cast(pd.DatetimeIndex, monthly_sum.index)
-
     min_date = dates.min()
 
-    stride = dopt_basics.datetime.timedelta_from_val(365, TimeUnitsTimedelta.DAYS)
-
-    dates = cast(pd.DatetimeIndex, monthly_sum.index)
-
-    min_date = dates.min()
-
-    # print("dates: ", dates)
     # ?? --- new: use monthly basis for time windows
     # baseline: 3 years - 36 months
-    # starting_date = datetime.datetime.now() - relativedelta(months=36)
-    starting_date = dates.max() - relativedelta(months=36)
-    # start_index = next(
-    #     (i for i, date in enumerate(dates) if date >= starting_date), len(dates) - 1
-    # )
-    # print("start idx: ", start_index, "length dates: ", len(dates))
-    starting_date = datetime.datetime.now() - relativedelta(months=12)
+    starting_date = datetime.datetime.now() - relativedelta(months=36)
     # starting_date = dates.max() - relativedelta(months=36)
-    # start_index = next(
-    #     (i for i, date in enumerate(dates) if date >= starting_date), len(dates) - 1
-    # )
-    # print("start idx: ", start_index, "length dates: ", len(dates))
 
     def get_index_date(
         dates: pd.DatetimeIndex,