class_docstr: Results class from estimated Theta Models.
  
  Parameters
  ----------
  b0 : float
      The estimated trend slope.
  alpha : float
      The estimated SES parameter.
  sigma2 : float
      The estimated residual variance from the SES/IMA model.
  one_step : float
      The one-step forecast from the SES.
  seasonal : ndarray
      An array of estimated seasonal terms.
  use_mle : bool
      A flag indicating that the parameters were estimated using MLE.
  model : ThetaModel
      The model used to produce the results.
class_docstr_tok: ["Results","class","from","estimated","Theta","Models",".","Parameters","--","--","--","--","--","b0",":","float","The","estimated","trend","slope",".","alpha",":","float","The","estimated","SES","parameter",".","sigma2",":","float","The","estimated","residual","variance","from","the","SES\/IMA","model",".","one_step",":","float","The","one-step","forecast","from","the","SES",".","seasonal",":","ndarray","An","array","of","estimated","seasonal","terms",".","use_mle",":","bool","A","flag","indicating","that","the","parameters","were","estimated","using","MLE",".","model",":","ThetaModel","The","model","used","to","produce","the","results","."]
docstr: The estimated residual variance
docstr_tok: ["The","estimated","residual","variance"]
code: def sigma2(self) -> float:
      """The estimated residual variance"""
      if self._sigma2 is None:
          mod = SARIMAX(self.model._y, order=(0, 1, 1), trend="c")
          res = mod.fit(disp=False)
          self._sigma2 = np.asarray(res.params)[-1]
      assert self._sigma2 is not None
      return self._sigma2
  
imports: from typing import TYPE_CHECKING, Optional
  import numpy
  import pandas
  from scipy import stats
  from statsmodels.iolib.summary import Summary
  from statsmodels.iolib.table import SimpleTable
  from statsmodels.tools.validation import array_like, bool_like, float_like, int_like, string_like
  from statsmodels.tsa.deterministic import DeterministicTerm
  from statsmodels.tsa.seasonal import seasonal_decompose
  from statsmodels.tsa.statespace.exponential_smoothing import ExponentialSmoothing
  from statsmodels.tsa.statespace.sarimax import SARIMAX
  from statsmodels.tsa.stattools import acf
  from statsmodels.tsa.tsatools import add_trend, freq_to_period
label_desc: Use image node_id 3 for calling the ThetaModelResults obj's underlying member method code with example usage: obj.sigma2() and returns: self