diff --git a/src/delta_barth/analysis/forecast.py b/src/delta_barth/analysis/forecast.py
index a9cf701..1cd3fd9 100644
--- a/src/delta_barth/analysis/forecast.py
+++ b/src/delta_barth/analysis/forecast.py
@@ -218,7 +218,8 @@ def _process_sales(
         [(year, month) for year in years for month in range(1, 13) if (year < current_year or (year == current_year and month <= current_month))], columns=["jahr", "monat"]
     )
 
-    monthly_sum = pd.merge(all_month_year_combinations, old_monthly_sum, on=["jahr", "monat"], how='left')
+    monthly_sum = pd.merge(all_month_year_combinations, old_monthly_sum, on=["jahr", "monat"], how="left")
+    monthly_sum[SALES_FEAT] = monthly_sum[SALES_FEAT].fillna(0)
     monthly_sum[DATE_FEAT] = (
         monthly_sum["monat"].astype(str) + "." + monthly_sum["jahr"].astype(str)
     )
@@ -228,7 +229,6 @@ def _process_sales(
     features = ["jahr", "monat"]
     target = SALES_FEAT
 
-    # ?? --- new: dates and forecast
     last_date = pd.to_datetime(datetime.datetime.now().strftime("%m.%Y"), format="%m.%Y")
     future_dates = pd.date_range(
         start=last_date + pd.DateOffset(months=1), periods=6, freq="MS"
@@ -248,7 +248,6 @@ def _process_sales(
         "early_stopping_rounds": [20, 50],
     }
 
-    # ?? --- new: best_estimator (internal usage only)
     best_estimator = None
     best_params: BestParametersXGBRegressor | None = None
     best_score_mae: float | None = float("inf")
@@ -285,9 +284,8 @@ def _process_sales(
         X_train, X_test = train[features], test[features]
         y_train, y_test = train[target], test[target]
 
-        # ?? --- new: adapted condition to fit new for-loop
         # test set size fixed at 6 --> first iteration: baseline - 6 entries
-        # for each new year 10 new data points needed
+        # for each new year 10 new data points (i.e., sales strictly positive) needed
         if len(train[train[SALES_FEAT] > 0]) >= 30 + 10 * add_year:
             too_few_month_points = False