class_docstr: Online Updating Ensemble of forecasters.
  
  Parameters
  ----------
  ensemble_algorithm : ensemble algorithm
  forecasters : list of (str, estimator) tuples
  n_jobs : int or None, optional (default=None)
      The number of jobs to run in parallel for fit. None means 1 unless
      in a joblib.parallel_backend context.
      -1 means using all processors.
class_docstr_tok: ["Online","Updating","Ensemble","of","forecasters",".","Parameters","--","--","--","--","--","ensemble_algorithm",":","ensemble","algorithm","forecasters",":","list","of","(","str",",","estimator",")","tuples","n_jobs",":","int","or","None",",","optional","(","default=None",")","The","number","of","jobs","to","run","in","parallel","for","fit",".","None","means","1","unless","in","a","joblib.parallel_backend","context",".","-1","means","using","all","processors","."]
code: def _predict(self, fh=None, X=None):
      if self.ensemble_algorithm is not None:
          self.weights = self.ensemble_algorithm.weights
      y_pred = (
          pd.concat(self._predict_forecasters(fh, X), axis=1)
          * self.weights
      )
      y_pred = y_pred.sum(axis=1)
      y_pred.name = self._y.name
      return y_pred
  
file_path: sktime/sktime/forecasting/online_learning/_online_ensemble.py
imports: import numpy
  import pandas
  from sktime.forecasting.compose._ensemble import EnsembleForecaster
label_desc: Use image node_id 5 for calling the OnlineEnsembleForecaster obj's underlying member method code with example usage: obj._predict(fh, X) and returns: y_pred