Implemented moving average

src/gluonts/model/trivial/mean.py

@@ -79,6 +79,54 @@ def predict_item(self, item: DataEntry) -> SampleForecast:
  )
 
 
+class MovingAveragePredictor(RepresentablePredictor):
+ """
+ A :class:`Predictor` that predicts the moving average based on the
+ last `context_length` elements of the input target.
+
+ If `prediction_length` = 1, the output is the moving average
+ based on the last `context_length` elements of the input target.
+
+ If `prediction_length` > 1, the output is the moving average based on the
+ last `context_length` elements of the input target, where
+ previously calculated moving averages are appended at the end of the input target.
+ Hence, for `prediction_length` larger than `context_length`, there will be
+ cases where the moving average is calculated on top of previous moving averages.
+
+ Parameters
+ ----------
+ context_length
+ Length of the target context used to condition the predictions.
+ prediction_length
+ Length of the prediction horizon.
+ freq
+ Frequency of the predicted data.
+ """
+
+ @validated()
+ def __init__(
+ self, prediction_length: int, freq: str, context_length: int,
+ ) -> None:
+ super().__init__(freq=freq, prediction_length=prediction_length)
+ self.context_length = context_length
+
+ def predict_item(self, item: DataEntry) -> SampleForecast:
+
+ target = item["target"].tolist()
+
+ for _ in range(self.prediction_length):
+ window = target[-self.context_length :]
+ target.append(np.nanmean(window))
+
+ start_date = frequency_add(item["start"], len(item["target"]))
+ return SampleForecast(
+ samples=np.array([target[-self.prediction_length :]]),
+ start_date=start_date,
+ freq=self.freq,
+ item_id=item.get(FieldName.ITEM_ID),
+ )
+
+
 class MeanEstimator(Estimator):
  """
  An `Estimator` that computes the mean targets in the training data,