diff --git a/src/gluonts/model/seasonal_agg/_predictor.py b/src/gluonts/model/seasonal_agg/_predictor.py
index c0ee4d0551..3cd3da38cf 100644
--- a/src/gluonts/model/seasonal_agg/_predictor.py
+++ b/src/gluonts/model/seasonal_agg/_predictor.py
@@ -29,16 +29,18 @@
 
 class SeasonalAggregatePredictor(RepresentablePredictor):
     """
-    Seasonal average forecaster.
+    Seasonal aggegate forecaster.
 
     For each time series :math:`y`, this predictor produces a forecast
-    :math:`\\tilde{y}(T+k) = y(T+k-h)`, where :math:`T` is the forecast time,
-    :math:`k = 0, ...,` `prediction_length - 1`, and :math:`h =`
-    `season_length`.
+    :math:`\\tilde{y}(T+k) = f\big(y(T+k-h), y(T+k-2h), \ldots,
+    y(T+k-mh)\big)`, where :math:`T` is the forecast time,
+    :math:`k = 0, ...,` `prediction_length - 1`, :math:`m =`num_seasons`,
+    :math:`h =`season_length` and :math:`f =`agg_fun`.
 
-    If `prediction_length > season_length`, then the season is repeated
-    multiple times. If a time series is shorter than season_length, then the
-    mean observed value is used as prediction.
+    If `prediction_length > season_length` :math:\times `num_seasons`, then the
+    seasonal aggregate is repeated multiple times. If a time series is shorter
+    than season_length` :math:\times `num_seasons`, then the `agg_fun` is
+    applied to the full time series.
 
     Parameters
     ----------
@@ -46,6 +48,10 @@ class SeasonalAggregatePredictor(RepresentablePredictor):
         Number of time points to predict.
     season_length
         Seasonality used to make predictions.
+    num_seasons
+        Number of seasons to aggregate.
+    agg_fun
+        Aggregate function.
     imputation_method
         The imputation method to use in case of missing values.
         Defaults to :py:class:`LastValueImputation` which replaces each missing
@@ -107,7 +113,6 @@ def predict_item(self, item: DataEntry) -> Forecast:
                 ]
                 for j in range(self.num_seasons)
             ]
-            print(indices)
             samples = self.agg_fun(target[indices], axis=0).reshape(
                 (1, self.prediction_length)
             )