class_docstr: Results for forecasting using STL to remove seasonality
  
  Parameters
  ----------
  stl : STL
      The STL instance used to decompose the data.
  result : DecomposeResult
      The result of applying STL to the data.
  model : Model
      The time series model used to model the non-seasonal dynamics.
  model_result : Results
      Model results instance supporting, at a minimum, ``forecast``.
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docstr: In-sample prediction and out-of-sample forecasting
  
  Parameters
  ----------
  start : int, str, or datetime, optional
      Zero-indexed observation number at which to start forecasting,
      i.e., the first forecast is start. Can also be a date string to
      parse or a datetime type. Default is the the zeroth observation.
  end : int, str, or datetime, optional
      Zero-indexed observation number at which to end forecasting, i.e.,
      the last forecast is end. Can also be a date string to
      parse or a datetime type. However, if the dates index does not
      have a fixed frequency, end must be an integer index if you
      want out of sample prediction. Default is the last observation in
      the sample.
  dynamic : bool, int, str, or datetime, optional
      Integer offset relative to `start` at which to begin dynamic
      prediction. Can also be an absolute date string to parse or a
      datetime type (these are not interpreted as offsets).
      Prior to this observation, true endogenous values will be used for
      prediction; starting with this observation and continuing through
      the end of prediction, forecasted endogenous values will be used
      instead.
  **kwargs
      Additional arguments may required for forecasting beyond the end
      of the sample. These arguments are passed into the time series
      model results' ``get_prediction`` method.
  
  Returns
  -------
  PredictionResults
      PredictionResults instance containing in-sample predictions,
      out-of-sample forecasts, and prediction intervals.
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code: def get_prediction(
      self,
      start: Optional[DateLike] = None,
      end: Optional[DateLike] = None,
      dynamic: Union[bool, DateLike] = False,
      **kwargs: dict[str, Any],
  ):
      """
      In-sample prediction and out-of-sample forecasting
  
      Parameters
      ----------
      start : int, str, or datetime, optional
          Zero-indexed observation number at which to start forecasting,
          i.e., the first forecast is start. Can also be a date string to
          parse or a datetime type. Default is the the zeroth observation.
      end : int, str, or datetime, optional
          Zero-indexed observation number at which to end forecasting, i.e.,
          the last forecast is end. Can also be a date string to
          parse or a datetime type. However, if the dates index does not
          have a fixed frequency, end must be an integer index if you
          want out of sample prediction. Default is the last observation in
          the sample.
      dynamic : bool, int, str, or datetime, optional
          Integer offset relative to `start` at which to begin dynamic
          prediction. Can also be an absolute date string to parse or a
          datetime type (these are not interpreted as offsets).
          Prior to this observation, true endogenous values will be used for
          prediction; starting with this observation and continuing through
          the end of prediction, forecasted endogenous values will be used
          instead.
      **kwargs
          Additional arguments may required for forecasting beyond the end
          of the sample. These arguments are passed into the time series
          model results' ``get_prediction`` method.
  
      Returns
      -------
      PredictionResults
          PredictionResults instance containing in-sample predictions,
          out-of-sample forecasts, and prediction intervals.
      """
      pred = self._model_result.get_prediction(
          start=start, end=end, dynamic=dynamic, **kwargs
      )
      seasonal_prediction = self._get_seasonal_prediction(
          start, end, dynamic
      )
      mean = pred.predicted_mean + seasonal_prediction
      try:
          var_pred_mean = pred.var_pred_mean
      except (AttributeError, NotImplementedError):
          # Allow models that do not return var_pred_mean
          import warnings
  
          warnings.warn(
              "The variance of the predicted mean is not available using "
              f"the {self.model.__class__.__name__} model class.",
              UserWarning,
              stacklevel=2,
          )
          var_pred_mean = np.nan + mean.copy()
      return PredictionResults(
          mean, var_pred_mean, dist="norm", row_labels=pred.row_labels
      )
  
imports: from statsmodels.compat.pandas import Substitution, is_int_index
  import datetime
  from typing import Any, Optional, Union
  import numpy
  import pandas
  from statsmodels.base.data import PandasData
  from statsmodels.iolib.summary import SimpleTable, Summary
  from statsmodels.tools.docstring import Docstring, Parameter, indent
  from statsmodels.tsa.base.prediction import PredictionResults
  from statsmodels.tsa.base.tsa_model import get_index_loc, get_prediction_index
  from statsmodels.tsa.seasonal import STL, DecomposeResult
  from statsmodels.tsa.statespace.kalman_filter import _check_dynamic
label_desc: Use image node_id 11 for calling the STLForecastResults obj's underlying member method code with example usage: obj.get_prediction(start, end, dynamic) and returns: PredictionResults