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: Summarize the model
  
  Returns
  -------
  Summary
      This holds the summary table and text, which can be printed or
      converted to various output formats.
  
  See Also
  --------
  statsmodels.iolib.summary.Summary
docstr_tok: ["Summarize","the","model","Returns","--","--","--","-","Summary","This","holds","the","summary","table","and","text",",","which","can","be","printed","or","converted","to","various","output","formats",".","See","Also","--","--","--","--","statsmodels.iolib.summary.Summary"]
code: def summary(self) -> Summary:
      """
      Summarize the model
  
      Returns
      -------
      Summary
          This holds the summary table and text, which can be printed or
          converted to various output formats.
  
      See Also
      --------
      statsmodels.iolib.summary.Summary
      """
      model = self.model
      smry = Summary()
  
      model_name = type(model).__name__
      title = model_name + " Results"
      method = "MLE" if self._use_mle else "OLS/SES"
  
      is_series = isinstance(model.endog_orig, pd.Series)
      index = getattr(model.endog_orig, "index", None)
      if is_series and isinstance(
          index, (pd.DatetimeIndex, pd.PeriodIndex)
      ):
          sample = [index[0].strftime("%m-%d-%Y")]
          sample += ["- " + index[-1].strftime("%m-%d-%Y")]
      else:
          sample = [str(0), str(model.endog_orig.shape[0])]
  
      dep_name = getattr(model.endog_orig, "name", "endog") or "endog"
      top_left = [
          ("Dep. Variable:", [dep_name]),
          ("Method:", [method]),
          ("Date:", None),
          ("Time:", None),
          ("Sample:", [sample[0]]),
          ("", [sample[1]]),
      ]
      method = (
          "Multiplicative"
          if model.method.startswith("mul")
          else "Additive"
      )
      top_right = [
          ("No. Observations:", [str(self._nobs)]),
          ("Deseasonalized:", [str(model.deseasonalize)]),
      ]
  
      if model.deseasonalize:
          top_right.extend(
              [
                  ("Deseas. Method:", [method]),
                  ("Period:", [str(model.period)]),
                  ("", [""]),
                  ("", [""]),
              ]
          )
      else:
          top_right.extend([("", [""])] * 4)
  
      smry.add_table_2cols(
          self, gleft=top_left, gright=top_right, title=title
      )
      table_fmt = {"data_fmts": ["%s", "%#0.4g"], "data_aligns": "r"}
  
      data = np.asarray(self.params)[:, None]
      st = SimpleTable(
          data,
          ["Parameters", "Estimate"],
          list(self.params.index),
          title="Parameter Estimates",
          txt_fmt=table_fmt,
      )
      smry.tables.append(st)
  
      return smry
  
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 7 for calling the ThetaModelResults obj's underlying member method code with example usage: obj.summary() and returns: smry