class_docstr: Inherits time series methods from TSM class.
  
  **** BETA-t-EGARCH IN MEAN MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the 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: Plots predictions with the estimated model
  
  Parameters
  ----------
  h : int (default : 5)
      How many steps ahead would you like to forecast?
  
  past_values : int (default : 20)
      How many past observations to show on the forecast graph?
  
  intervals : Boolean
      Would you like to show prediction intervals for the forecast?
  
  Returns
  ----------
  - Plot of the forecast
docstr_tok: ["Plots","predictions","with","the","estimated","model","Parameters","--","--","--","--","--","h",":","int","(","default",":","5",")","How","many","steps","ahead","would","you","like","to","forecast","?","past_values",":","int","(","default",":","20",")","How","many","past","observations","to","show","on","the","forecast","graph","?","intervals",":","Boolean","Would","you","like","to","show","prediction","intervals","for","the","forecast","?","Returns","--","--","--","--","--","-","Plot","of","the","forecast"]
code: def plot_predict(self, h=5, past_values=20, intervals=True, **kwargs):
      """Plots predictions with the estimated model
  
      Parameters
      ----------
      h : int (default : 5)
          How many steps ahead would you like to forecast?
  
      past_values : int (default : 20)
          How many past observations to show on the forecast graph?
  
      intervals : Boolean
          Would you like to show prediction intervals for the forecast?
  
      Returns
      ----------
      - Plot of the forecast
      """
      import matplotlib.pyplot as plt
      import seaborn as sns
  
      figsize = kwargs.get("figsize", (10, 7))
  
      if self.latent_variables.estimated is False:
          raise Exception("No latent variables estimated!")
      else:
          # Retrieve data, dates and (transformed) latent variables
          lmda, Y, scores = self._model(
              self.latent_variables.get_z_values()
          )
          date_index = self.shift_dates(h)
  
          if self.latent_variables.estimation_method in ["M-H"]:
              sim_vector = self._sim_prediction_bayes(h, 15000)
              error_bars = []
  
              for pre in range(5, 100, 5):
                  error_bars.append(
                      np.insert(
                          [np.percentile(i, pre) for i in sim_vector],
                          0,
                          lmda[-1],
                      )
                  )
  
              forecasted_values = np.insert(
                  [np.mean(i) for i in sim_vector], 0, lmda[-1]
              )
              plot_values = np.append(
                  lmda[-1 - past_values : -2], forecasted_values
              )
              plot_index = date_index[-h - past_values :]
  
          else:
              t_z = self.transform_z()
              sim_values = self._sim_prediction(
                  lmda, Y, scores, h, t_z, 15000
              )
              (
                  error_bars,
                  forecasted_values,
                  plot_values,
                  plot_index,
              ) = self._summarize_simulations(
                  lmda, sim_values, date_index, h, past_values
              )
  
          plt.figure(figsize=figsize)
          if intervals == True:
              alpha = [0.15 * i / float(100) for i in range(50, 12, -2)]
              for count, pre in enumerate(error_bars):
                  plt.fill_between(
                      date_index[-h - 1 :],
                      np.exp(error_bars[count] / 2.0),
                      np.exp(error_bars[-count - 1] / 2.0),
                      alpha=alpha[count],
                  )
  
          plt.plot(plot_index, np.exp(plot_values / 2.0))
          plt.title(
              "Forecast for "
              + self.data_name
              + " Conditional Volatility"
          )
          plt.xlabel("Time")
          plt.ylabel(self.data_name + " Conditional Volatility")
          plt.show()
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  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 14 for calling the EGARCHM obj's underlying member method code with example usage: obj.plot_predict(h, past_values, intervals) without return types