code: def plot_predict_is(self, h=5, fit_once=True, fit_method='MLE', **kwargs):
          
          import matplotlib.pyplot as plt
          import seaborn as sns
  
          figsize = kwargs.get('figsize',(10,7))
  
          plt.figure(figsize=figsize)
          predictions = self.predict_is(h, fit_once=fit_once, fit_method=fit_method)
          data = self.data[-h:]
          plt.plot(predictions.index,data,label='Data')
          plt.plot(predictions.index,predictions,label='Predictions',c='black')
          plt.title(self.data_name)
          plt.legend(loc=2)   
          plt.show()
docs: 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