class_docstr: Base series annotator.
  
  Parameters
  ----------
  fmt : str {"dense", "sparse"}, optional (default="dense")
      Annotation output format:
      * If "sparse", a sub-series of labels for only the outliers in X is returned,
      * If "dense", a series of labels for all values in X is returned.
  labels : str {"indicator", "score"}, optional (default="indicator")
      Annotation output labels:
      * If "indicator", returned values are boolean, indicating whether a value is an
      outlier,
      * If "score", returned values are floats, giving the outlier score.
  
  Notes
  -----
  Assumes "predict" data is temporal future of "fit"
  Single time series in both, no meta-data.
  
  The base series annotator specifies the methods and method
  signatures that all annotators have to implement.
  
  Specific implementations of these methods is deferred to concrete
  annotators.
docstr: Update model with new data and optional ground truth annotations.
  
  Parameters
  ----------
  X : pd.DataFrame
      Training data to update model with (time series).
  Y : pd.Series, optional
      Ground truth annotations for training if annotator is supervised.
  
  Returns
  -------
  self :
      Reference to self.
  
  Notes
  -----
  Updates fitted model that updates attributes ending in "_".
docstr_tok: ["Update","model","with","new","data","and","optional","ground","truth","annotations",".","Parameters","--","--","--","--","--","X",":","pd.DataFrame","Training","data","to","update","model","with","(","time","series",")",".","Y",":","pd.Series",",","optional","Ground","truth","annotations","for","training","if","annotator","is","supervised",".","Returns","--","--","--","-","self",":","Reference","to","self",".","Notes","--","--","-","Updates","fitted","model","that","updates","attributes","ending","in","``","_","''","."]
code: def update(self, X, Y=None):
      """Update model with new data and optional ground truth annotations.
  
      Parameters
      ----------
      X : pd.DataFrame
          Training data to update model with (time series).
      Y : pd.Series, optional
          Ground truth annotations for training if annotator is supervised.
  
      Returns
      -------
      self :
          Reference to self.
  
      Notes
      -----
      Updates fitted model that updates attributes ending in "_".
      """
      self.check_is_fitted()
  
      X = check_series(X)
  
      if Y is not None:
          Y = check_series(Y)
  
      self._X = X.combine_first(self._X)
  
      if Y is not None:
          self._Y = Y.combine_first(self._Y)
  
      self._update(X=X, Y=Y)
  
      return self
  
file_path: sktime/sktime/annotation/base/_base.py
imports: from sktime.base import BaseEstimator
  from sktime.utils.validation.annotation import check_fmt, check_labels
  from sktime.utils.validation.series import check_series
label_desc: Use image node_id 5 for calling the BaseSeriesAnnotator obj's underlying member method code with example usage: obj.update(X, Y) and returns: self