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","."]
code: def __init__(
      self,
      b0: float,
      alpha: float,
      sigma2: Optional[float],
      one_step: float,
      seasonal: np.ndarray,
      use_mle: bool,
      model: ThetaModel,
  ) -> None:
      self._b0 = b0
      self._alpha = alpha
      self._sigma2 = sigma2
      self._one_step = one_step
      self._nobs = model.endog_orig.shape[0]
      self._model = model
      self._seasonal = seasonal
      self._use_mle = use_mle
  
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 1 to create a new ThetaModelResults object  with example: obj = ThetaModelResults(b0, alpha, sigma2, one_step, seasonal, use_mle, model)