expose aggregation method, fix forecast shape

src/gluonts/mx/model/n_beats/_ensemble.py

@@ -183,7 +183,7 @@ def predict(
                 # get the forecast start date
                 if start_date is None:
                     start_date = prediction.start_date
-            output = np.stack(output, axis=0)
+            output = np.concatenate(output, axis=0)
 
             # aggregating output of different models
             # default according to paper is median,
@@ -311,6 +311,10 @@ class NBEATSEnsembleEstimator(Estimator):
         (trend). A list of strings of length 1 or 'num_stacks'.
         Default and recommended value for generic mode: ["G"]
         Recommended value for interpretable mode: ["T","S"]
+    aggregation_method
+        The method by which to aggregate the individual predictions of the
+        models. Either 'median', 'mean' or 'none', in which case no aggregation
+        happens. Default is 'median'.
     **kwargs
         Arguments passed down to the individual estimators.
     """
@@ -335,6 +339,7 @@ def __init__(
         expansion_coefficient_lengths: Optional[List[int]] = None,
         sharing: Optional[List[bool]] = None,
         stack_types: Optional[List[str]] = None,
+        aggregation_method: str = "median",
         **kwargs,
     ) -> None:
         super().__init__()
@@ -384,6 +389,7 @@ def __init__(
         self.expansion_coefficient_lengths = expansion_coefficient_lengths
         self.sharing = sharing
         self.stack_types = stack_types
+        self.aggregation_method = aggregation_method
 
         # Actually instantiate the different models
         self.estimators = self._estimator_factory(**kwargs)
@@ -468,7 +474,11 @@ def _train(
                     )
                 )
 
-        return NBEATSEnsemblePredictor(self.prediction_length, predictors)
+        return NBEATSEnsemblePredictor(
+            self.prediction_length,
+            predictors,
+            aggregation_method=self.aggregation_method,
+        )
 
     def train(
         self, training_data: Dataset, validation_data: Optional[Dataset] = None