class_docstr: Aggregation Algorithm
  
  Parameters
  ----------
  learning_rate : float
      Learning rate for the Aggregation algorithm
  
  loss_type : string
      'absolute' or 'squared'
  
  match_window : int
      (default: 10) how many of the observations at the end of a model time 
      series to assess whether the data being used in each model is the same
docstr: Calculates squared loss
  
  Parameters
  ----------
  data : np.ndarray
      Univariate data
  
  predictions : np.ndarray
      Univariate predictions
  
  Returns
  ----------
  - np.ndarray of the squared loss
docstr_tok: ["Calculates","squared","loss","Parameters","--","--","--","--","--","data",":","np.ndarray","Univariate","data","predictions",":","np.ndarray","Univariate","predictions","Returns","--","--","--","--","--","-","np.ndarray","of","the","squared","loss"]
code: def squared_loss(data, predictions):
      """Calculates squared loss
  
      Parameters
      ----------
      data : np.ndarray
          Univariate data
  
      predictions : np.ndarray
          Univariate predictions
  
      Returns
      ----------
      - np.ndarray of the squared loss
      """
      return np.square(data - predictions)
  
file_path: pyflux/pyflux/ensembles/mixture_of_experts.py
imports: import pandas
  import numpy
label_desc: Use image node_id 3 for calling the Aggregate obj's underlying member method code with example usage: obj.squared_loss(data, predictions) and returns: np