class_docstr: The simple conditional residuals uncertainty model.
  For more details, see `~greykite.algo.uncertainty.conditional.conf_interval`.
  
  Attributes
  ----------
  uncertainty_dict : `dict` [`str`, any]
      The uncertainty model specification. It should have the following keys:
  
              "uncertainty_method": a string that is in
                  `~greykite.sklearn.uncertainty.uncertainty_methods.UncertaintyMethodEnum`.
              "params": a dictionary that includes any additional parameters needed by the uncertainty method.
  
  UNCERTAINTY_METHOD : `str`
      The name for the uncertainty method.
  REQUIRED_PARAMS : `list` [`str`]
      A list of required parameters for the method.
  coverage : `float`
      The coverage of the uncertainty intervals.
      Will be used to calculate the quantiles.
  time_col : `str`
      The column name for timestamps in ``train_df`` and ``fut_df``.
  value_col : `str`
      The column name for values in ``train_df`` and ``fut_df``.
  distribution_col : `str` or None
      The column name for the column used to fit the distribution in ``train_df`` and ``fut_df``.
  residual_col : `str` or None
      The column name for residuals in ``train_df``.
  conditional_cols : `list` [`str`]
      The conditional columns when calculating the standard errors of the residuals.
  offset_col : `str` or None
      The column name for the column used to offset predicted intervals.
  is_residual_based : `bool` or None
      Whether to fit the original values or residuals.
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code: def __init__(
      self,
      uncertainty_dict: Dict[str, any],
      coverage: Optional[float] = None,
      time_col: Optional[str] = None,
      **kwargs,
  ):
      super().__init__(uncertainty_dict=uncertainty_dict, **kwargs)
  
      self.coverage = coverage
      self.time_col = time_col
  
      # Set by ``fit`` method.
      self.distribution_col: Optional[str] = None
      self.value_col: Optional[str] = None
      self.predicted_col: Optional[str] = None
      self.residual_col: Optional[str] = None
      self.conditional_cols: Optional[List[str]] = None
      self.offset_col: Optional[str] = None
      self.is_residual_based: Optional[bool] = None
  
      # Set by ``predict`` method
      self.pred_df = None
  
file_path: greykite/greykite/sklearn/uncertainty/simple_conditional_residuals_model.py
imports: from typing import Dict
  from typing import List
  from typing import Optional
  import pandas
  from greykite.algo.forecast.silverkite.forecast_silverkite_helper import get_silverkite_uncertainty_dict
  from greykite.algo.uncertainty.conditional.conf_interval import conf_interval
  from greykite.algo.uncertainty.conditional.conf_interval import predict_ci
  from greykite.common import constants
  from greykite.common.features.timeseries_features import add_time_features_df
  from greykite.sklearn.uncertainty.base_uncertainty_model import BaseUncertaintyModel
  from greykite.sklearn.uncertainty.exceptions import UncertaintyError
label_desc: Use image node_id 1 to create a new SimpleConditionalResidualsModel object from inherited base classes: BaseUncertaintyModel with example: obj = SimpleConditionalResidualsModel(uncertainty_dict, coverage, time_col)