class_docstr: An 'Auto Regressive Integrated (Random) Forest'
code: def fit(self, losses, configs=None):
      if configs is None:
          configs = [[]] * len(times)
  
      # convert learning curves into X and y data
  
      X = []
      y = []
  
      for l, c in zip(losses, configs):
          l = self.apply_differencing(l)
  
          for i in range(self.order, len(l)):
              X.append(np.hstack([l[i - self.order : i], c]))
              y.append(l[i])
  
      self.X = np.array(X)
      self.y = np.array(y)
  
      self.rfr = rfr().fit(self.X, self.y)
  
file_path: auptimizer/src/aup/Proposer/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 4 for calling the ARIF obj's underlying member method code with example usage: obj.fit(losses, configs) without return types