class_docstr: Inherits time series methods from the TSM parent class.
  
  **** AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.ndarray
      Field to specify the univariate time series data that will be used.
  
  ar : int
      Field to specify how many AR lags the model will have.
  
  ma : int
      Field to specify how many MA lags the model will have.
  
  integ : int (default : 0)
      Specifies how many times to difference the time series.
  
  target : str (if data is a pd.DataFrame) or int (if data is a np.ndarray)
      Specifies which column name or array index to use. By default, first
      column/array index will be selected as the dependent variable.
  
  family : family object
      E.g pf.Normal(), pf.t(), pf.Laplace()...
docstr: Produces simulation forecasted values and prediction intervals. 
  
  This is a utility function that constructs the prediction intervals and other quantities 
  used for plot_predict() in particular.
  
  Parameters
  ----------
  mean_values : np.ndarray
      Mean predictions for h-step ahead forecasts
  
  sim_vector : np.ndarray
      N simulated predictions for h-step ahead forecasts
  
  date_index : pd.DateIndex or np.ndarray
      Date index for the simulations
  
  h : int
      How many steps ahead to forecast
  
  past_values : int
      How many past observations to include in the forecast plot
docstr_tok: ["Produces","simulation","forecasted","values","and","prediction","intervals",".","This","is","a","utility","function","that","constructs","the","prediction","intervals","and","other","quantities","used","for","plot_predict","(",")","in","particular",".","Parameters","--","--","--","--","--","mean_values",":","np.ndarray","Mean","predictions","for","h-step","ahead","forecasts","sim_vector",":","np.ndarray","N","simulated","predictions","for","h-step","ahead","forecasts","date_index",":","pd.DateIndex","or","np.ndarray","Date","index","for","the","simulations","h",":","int","How","many","steps","ahead","to","forecast","past_values",":","int","How","many","past","observations","to","include","in","the","forecast","plot"]
code: def _summarize_simulations(
      self, mean_values, sim_vector, date_index, h, past_values
  ):
      """Produces simulation forecasted values and prediction intervals.
  
      This is a utility function that constructs the prediction intervals and other quantities
      used for plot_predict() in particular.
  
      Parameters
      ----------
      mean_values : np.ndarray
          Mean predictions for h-step ahead forecasts
  
      sim_vector : np.ndarray
          N simulated predictions for h-step ahead forecasts
  
      date_index : pd.DateIndex or np.ndarray
          Date index for the simulations
  
      h : int
          How many steps ahead to forecast
  
      past_values : int
          How many past observations to include in the forecast plot
      """
  
      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],
              )
          )
      if self.latent_variables.estimation_method in ["M-H"]:
          forecasted_values = np.insert(
              [np.mean(i) for i in sim_vector], 0, mean_values[-h - 1]
          )
      else:
          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 output
  from ..None import tests
  from ..None import tsm
  from ..None import data_check
  from .arma_recursions import arima_recursion, arima_recursion_normal, arima_recursion_poisson
label_desc: Use image node_id 15 for calling the ARIMA 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