class_docstr: Inherits time series methods from TSM parent class.
  
  **** skew BETA-t-EGARCH IN MEAN MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the univariate time series data that will be used.
  
  p : int
      Field to specify how many GARCH terms the model will have.
  
  q : int
      Field to specify how many SCORE terms the model will have.
  
  target : str (pd.DataFrame) or int (np.array)
      Specifies which column name or array index to use. By default, first
      column/array will be selected as the dependent variable.
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docstr: Summarizes a simulation vector and a mean vector of predictions
  
  Parameters
  ----------
  lmda : np.array
      Past volatility values for the moedl
  
  sim_vector : np.array
      N simulation predictions for h-step ahead forecasts
  
  date_index : pd.DateIndex or np.array
      Dates for the simulations
  
  h : int
      How many steps ahead are forecast
  
  past_values : int
      How many past observations to include in the forecast plot
  
  intervals : Boolean
      Would you like to show prediction intervals for the forecast?
docstr_tok: ["Summarizes","a","simulation","vector","and","a","mean","vector","of","predictions","Parameters","--","--","--","--","--","lmda",":","np.array","Past","volatility","values","for","the","moedl","sim_vector",":","np.array","N","simulation","predictions","for","h-step","ahead","forecasts","date_index",":","pd.DateIndex","or","np.array","Dates","for","the","simulations","h",":","int","How","many","steps","ahead","are","forecast","past_values",":","int","How","many","past","observations","to","include","in","the","forecast","plot","intervals",":","Boolean","Would","you","like","to","show","prediction","intervals","for","the","forecast","?"]
code: def _summarize_simulations(
      self, lmda, sim_vector, date_index, h, past_values
  ):
      """Summarizes a simulation vector and a mean vector of predictions
  
      Parameters
      ----------
      lmda : np.array
          Past volatility values for the moedl
  
      sim_vector : np.array
          N simulation predictions for h-step ahead forecasts
  
      date_index : pd.DateIndex or np.array
          Dates for the simulations
  
      h : int
          How many steps ahead are forecast
  
      past_values : int
          How many past observations to include in the forecast plot
  
      intervals : Boolean
          Would you like to show prediction intervals for the forecast?
      """
      mean_values = np.append(
          lmda, np.array([np.mean(i) for i in sim_vector])
      )
  
      error_bars = []
      for pre in range(5, 100, 5):
          error_bars.append(
              np.insert(
                  [np.percentile(i, pre) for i in sim_vector],
                  0,
                  mean_values[-h - 1],
              )
          )
      forecasted_values = np.insert(
          [np.mean(i) for i in sim_vector], 0, mean_values[-h - 1]
      )
      plot_values = mean_values[-h - past_values :]
      plot_index = date_index[-h - past_values :]
  
      return error_bars, forecasted_values, plot_values, plot_index
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  import scipy.special
  from ..None import families
  from ..None import output
  from ..None import tests
  from ..None import tsm
  from ..None import gas
  from ..None import data_check
label_desc: Use image node_id 9 for calling the SEGARCHM obj's underlying member method code with example usage: obj._summarize_simulations(lmda, sim_vector, date_index, h, past_values) and returns: error_bars,  forecasted_values,  plot_values,  plot_index