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 __init__(self, forecasters, ensemble_algorithm=None, n_jobs=None):
      self.n_jobs = n_jobs
      self.ensemble_algorithm = ensemble_algorithm
  
      super(EnsembleForecaster, self).__init__(
          forecasters=forecasters, n_jobs=n_jobs
      )
  
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 1 to create a new OnlineEnsembleForecaster object from inherited base classes: EnsembleForecaster with example: obj = OnlineEnsembleForecaster(forecasters, ensemble_algorithm, n_jobs)