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``.
class_docstr_tok: ["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","``","."]
docstr: Out-of-sample forecasts
  
  Parameters
  ----------
  steps : int, str, or datetime, optional
      If an integer, the number of steps to forecast from the end of the
      sample. Can also be a date string to parse or a datetime type.
      However, if the dates index does not have a fixed frequency, steps
      must be an integer. Default
  **kwargs
      Additional arguments may required for forecasting beyond the end
      of the sample. These arguments are passed into the time series
      model results' ``forecast`` method.
  
  Returns
  -------
  forecast : {ndarray, Series}
      Out of sample forecasts
docstr_tok: ["Out-of-sample","forecasts","Parameters","--","--","--","--","--","steps",":","int",",","str",",","or","datetime",",","optional","If","an","integer",",","the","number","of","steps","to","forecast","from","the","end","of","the","sample",".","Can","also","be","a","date","string","to","parse","or","a","datetime","type",".","However",",","if","the","dates","index","does","not","have","a","fixed","frequency",",","steps","must","be","an","integer",".","Default","*","*","kwargs","Additional","arguments","may","required","for","forecasting","beyond","the","end","of","the","sample",".","These","arguments","are","passed","into","the","time","series","model","results","'","``","forecast","``","method",".","Returns","--","--","--","-","forecast",":","{","ndarray",",","Series","}","Out","of","sample","forecasts"]
code: def forecast(
      self, steps: int = 1, **kwargs: dict[str, Any]
  ) -> Union[np.ndarray, pd.Series]:
      """
      Out-of-sample forecasts
  
      Parameters
      ----------
      steps : int, str, or datetime, optional
          If an integer, the number of steps to forecast from the end of the
          sample. Can also be a date string to parse or a datetime type.
          However, if the dates index does not have a fixed frequency, steps
          must be an integer. Default
      **kwargs
          Additional arguments may required for forecasting beyond the end
          of the sample. These arguments are passed into the time series
          model results' ``forecast`` method.
  
      Returns
      -------
      forecast : {ndarray, Series}
          Out of sample forecasts
      """
      forecast = self._model_result.forecast(steps=steps, **kwargs)
      index = (
          forecast.index if isinstance(forecast, pd.Series) else None
      )
      return forecast + self._seasonal_forecast(steps, index)
  
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 10 for calling the STLForecastResults obj's underlying member method code with example usage: obj.forecast(steps) and returns: unknown