class_docstr: Class to hold results from fitting an unobserved components model.
  
  Parameters
  ----------
  model : UnobservedComponents instance
      The fitted model instance
  
  Attributes
  ----------
  specification : dictionary
      Dictionary including all attributes from the unobserved components
      model instance.
  
  See Also
  --------
  statsmodels.tsa.statespace.kalman_filter.FilterResults
  statsmodels.tsa.statespace.mlemodel.MLEResults
class_docstr_tok: ["Class","to","hold","results","from","fitting","an","unobserved","components","model",".","Parameters","--","--","--","--","--","model",":","UnobservedComponents","instance","The","fitted","model","instance","Attributes","--","--","--","--","--","specification",":","dictionary","Dictionary","including","all","attributes","from","the","unobserved","components","model","instance",".","See","Also","--","--","--","--","statsmodels.tsa.statespace.kalman_filter.FilterResults","statsmodels.tsa.statespace.mlemodel.MLEResults"]
code: def summary(self, alpha=0.05, start=None):
      # Create the model name
  
      model_name = [self.specification.trend_specification]
  
      if self.specification.seasonal:
          seasonal_name = (
              "seasonal(%d)" % self.specification.seasonal_periods
          )
          if self.specification.stochastic_seasonal:
              seasonal_name = "stochastic " + seasonal_name
          model_name.append(seasonal_name)
  
      if self.specification.freq_seasonal:
          for ix, is_stochastic in enumerate(
              self.specification.stochastic_freq_seasonal
          ):
              periodicity = self.specification.freq_seasonal_periods[ix]
              harmonics = self.specification.freq_seasonal_harmonics[ix]
              freq_seasonal_name = "freq_seasonal({p}({h}))".format(
                  p=repr(periodicity), h=repr(harmonics)
              )
              if is_stochastic:
                  freq_seasonal_name = (
                      "stochastic " + freq_seasonal_name
                  )
              model_name.append(freq_seasonal_name)
  
      if self.specification.cycle:
          cycle_name = "cycle"
          if self.specification.stochastic_cycle:
              cycle_name = "stochastic " + cycle_name
          if self.specification.damped_cycle:
              cycle_name = "damped " + cycle_name
          model_name.append(cycle_name)
  
      if self.specification.autoregressive:
          autoregressive_name = "AR(%d)" % self.specification.ar_order
          model_name.append(autoregressive_name)
  
      return super().summary(
          alpha=alpha,
          start=start,
          title="Unobserved Components Results",
          model_name=model_name,
      )
  
imports: from warnings import warn
  import numpy
  from statsmodels.compat.pandas import Appender
  from statsmodels.tools.tools import Bunch
  from statsmodels.tools.sm_exceptions import OutputWarning, SpecificationWarning
  import statsmodels.base.wrapper
  from statsmodels.tsa.filters.hp_filter import hpfilter
  from statsmodels.tsa.tsatools import lagmat
  from .mlemodel import MLEModel, MLEResults, MLEResultsWrapper
  from .initialization import Initialization
  from .tools import companion_matrix, constrain_stationary_univariate, unconstrain_stationary_univariate, prepare_exog
label_desc: Use image node_id 10 for calling the UnobservedComponentsResults obj's underlying member method code with example usage: obj.summary(alpha, start) and returns: super