class_docstr: Inherits time series methods from TSM class.
  
  **** skew BETA-t-EGARCH 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 forecasts with the estimated model against data (Simulated prediction with data)
  
  Parameters
  ----------
  h : int (default : 5)
      How many steps to forecast
  
  fit_once : boolean
      (default: True) Fits only once before the in-sample prediction; if False, fits after every new datapoint
  
  fit_method : string
      Which method to fit the model with
  
  Returns
  ----------
  - Plot of the forecast against data 
docstr_tok: ["Plots","forecasts","with","the","estimated","model","against","data","(","Simulated","prediction","with","data",")","Parameters","--","--","--","--","--","h",":","int","(","default",":","5",")","How","many","steps","to","forecast","fit_once",":","boolean","(","default",":","True",")","Fits","only","once","before","the","in-sample","prediction",";","if","False",",","fits","after","every","new","datapoint","fit_method",":","string","Which","method","to","fit","the","model","with","Returns","--","--","--","--","--","-","Plot","of","the","forecast","against","data"]
code: def plot_predict_is(
      self, h=5, fit_once=True, fit_method="MLE", **kwargs
  ):
      """Plots forecasts with the estimated model against data (Simulated prediction with data)
  
      Parameters
      ----------
      h : int (default : 5)
          How many steps to forecast
  
      fit_once : boolean
          (default: True) Fits only once before the in-sample prediction; if False, fits after every new datapoint
  
      fit_method : string
          Which method to fit the model with
  
      Returns
      ----------
      - Plot of the forecast against data
      """
      import matplotlib.pyplot as plt
      import seaborn as sns
  
      figsize = kwargs.get("figsize", (10, 7))
  
      plt.figure(figsize=figsize)
      date_index = self.index[-h:]
      predictions = self.predict_is(
          h, fit_method=fit_method, fit_once=fit_once
      )
      data = self.data[-h:]
  
      t_params = self.transform_z()
      loc = t_params[-1] + (
          t_params[-3] - (1.0 / t_params[-3])
      ) * predictions.values.T[0] * (
          np.sqrt(t_params[-2]) * sp.gamma((t_params[-2] - 1.0) / 2.0)
      ) / (
          np.sqrt(np.pi) * sp.gamma(t_params[-2] / 2.0)
      )
  
      plt.plot(date_index, np.abs(data - loc), label="Data")
      plt.plot(date_index, predictions, label="Predictions", c="black")
      plt.title(self.data_name)
      plt.legend(loc=2)
      plt.show()
  
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 16 for calling the SEGARCH obj's underlying member method code with example usage: obj.plot_predict_is(h, fit_once, fit_method) without return types