class_docstr: An 'Auto Regressive Integrated (Random) Forest'
class_docstr_tok: ["An","'Auto","Regressive","Integrated","(","Random",")","Forest","'"]
docstr: function to invert the differencing
docstr_tok: ["function","to","invert","the","differencing"]
code: def invert_differencing(
      self, initial_part, differenced_rest, order=None
  ):
      """
      function to invert the differencing
      """
  
      if order is None:
          order = self.diff_order
  
      # compute the differenced values of the initial part:
      starting_points = [
          self.apply_differencing(initial_part, order=order)[-1]
          for order in range(self.diff_order)
      ]
  
      actual_predictions = differenced_rest
      import pdb
  
      pdb.set_trace()
      for s in starting_points[::-1]:
          actual_predictions = np.cumsum(
              np.hstack([s, actual_predictions])
          )[1:]
  
      return actual_predictions
  
file_path: HpBandSter/hpbandster/optimizers/learning_curve_models/arif.py
imports: import sys
  import numpy
  from sklearn.ensemble import RandomForestRegressor
  from hpbandster.learning_curve_models.base import LCModel
  from IPython import embed
label_desc: Use image node_id 3 for calling the ARIF obj's underlying member method code with example usage: obj.invert_differencing(initial_part, differenced_rest, order) and returns: actual_predictions