class_docstr: Base class of Aggregators that do need training.
class_docstr_tok: ["Base","class","of","Aggregators","that","do","need","training","."]
docstr: Aggregates the (sequence of) multivariate binary series given as
  input into a (sequence of) univariate binary series.
  
  Parameters
  ----------
  series
      The (sequence of) multivariate binary series to aggregate
  
  Returns
  -------
  TimeSeries
      (Sequence of) aggregated results
docstr_tok: ["Aggregates","the","(","sequence","of",")","multivariate","binary","series","given","as","input","into","a","(","sequence","of",")","univariate","binary","series",".","Parameters","--","--","--","--","--","series","The","(","sequence","of",")","multivariate","binary","series","to","aggregate","Returns","--","--","--","-","TimeSeries","(","Sequence","of",")","aggregated","results"]
code: def predict(
      self, series: Union[TimeSeries, Sequence[TimeSeries]]
  ) -> Union[TimeSeries, Sequence[TimeSeries]]:
      """Aggregates the (sequence of) multivariate binary series given as
      input into a (sequence of) univariate binary series.
  
      Parameters
      ----------
      series
          The (sequence of) multivariate binary series to aggregate
  
      Returns
      -------
      TimeSeries
          (Sequence of) aggregated results
      """
      self._assert_fit_called()
      list_series = self._check_input(series)
  
      raise_if_not(
          all([s.width == self.width_trained_on for s in list_series]),
          "all series in `series` must have the same number of components as the data"
          + " used for training the detector model, number of components in training:"
          + f" {self.width_trained_on}.",
      )
  
      if isinstance(series, TimeSeries):
          return self._predict_core(list_series)[0]
      else:
          return self._predict_core(list_series)
  
imports: from abc import ABC, abstractmethod
  from typing import Any, Sequence, Union
  import numpy
  from darts import TimeSeries
  from darts.ad.utils import _to_list, eval_accuracy_from_binary_prediction
  from darts.logging import raise_if_not
label_desc: Use image node_id 4 for calling the FittableAggregator obj's underlying member method code with example usage: obj.predict(series) and returns: self, self