class_docstr: Inherits time series methods from TSM class.
  
  **** GENERALIZED AUTOREGRESSIVE SCORE LOCAL LINEAR TREND MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the univariate time series data that will be used.
  
  integ : int (default : 0)
      Specifies how many time to difference the time series.
  
  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.
  
  dist : str
      Which distribution to use
docstr: Summarizes a simulation vector and a mean vector of predictions
  
  Parameters
  ----------
  mean_values : np.array
      Mean predictions for h-step ahead forecasts
  
  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","--","--","--","--","--","mean_values",":","np.array","Mean","predictions","for","h-step","ahead","forecasts","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, mean_values, sim_vector, date_index, h, past_values
  ):
      """Summarizes a simulation vector and a mean vector of predictions
  
      Parameters
      ----------
      mean_values : np.array
          Mean predictions for h-step ahead forecasts
  
      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?
      """
  
      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 = 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 tsm
  from ..None import data_check
  from .gas_core_recursions import gas_llt_recursion
label_desc: Use image node_id 15 for calling the GASLLT obj's underlying member method code with example usage: obj._summarize_simulations(mean_values, sim_vector, date_index, h, past_values) and returns: error_bars,  forecasted_values,  plot_values,  plot_index