class_docstr: Prediction results
  
  Parameters
  ----------
  predicted_mean : {ndarray, Series, DataFrame}
      The predicted mean values
  var_pred_mean : {ndarray, Series, DataFrame}
      The variance of the predicted mean values
  dist : {None, "norm", "t", rv_frozen}
      The distribution to use when constructing prediction intervals.
      Default is normal.
  df : int, optional
      The degree of freedom parameter for the t. Not used if dist is None,
      "norm" or a callable.
  row_labels : {Sequence[Hashable], pd.Index}
      Row labels to use for the summary frame. If None, attempts to read the
      index of ``predicted_mean``
code: def __init__(
      self,
      predicted_mean,
      var_pred_mean,
      dist=None,
      df=None,
      row_labels=None,
  ):
      self._predicted_mean = np.asarray(predicted_mean)
      self._var_pred_mean = np.asarray(var_pred_mean)
      self._df = df
      self._row_labels = row_labels
      if row_labels is None:
          self._row_labels = getattr(predicted_mean, "index", None)
      self._use_pandas = self._row_labels is not None
  
      if dist != "t" and df is not None:
          raise ValueError('df must be None when dist is not "t"')
  
      if dist is None or dist == "norm":
          self.dist = stats.norm
          self.dist_args = ()
      elif dist == "t":
          self.dist = stats.t
          self.dist_args = (self._df,)
      elif isinstance(dist, stats.distributions.rv_frozen):
          self.dist = dist
          self.dist_args = ()
      else:
          raise ValueError(
              'dist must be a None, "norm", "t" or a callable.'
          )
  
file_path: statsmodels/statsmodels/tsa/base/prediction.py
imports: import numpy
  import pandas
  from scipy import stats
label_desc: Use image node_id 1 to create a new PredictionResults object  with example: obj = PredictionResults(predicted_mean, var_pred_mean, dist, df, row_labels)