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: 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)
      predictions = self.predict_is(
          h=h, fit_method=fit_method, fit_once=fit_once
      )
      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()
  
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 21 for calling the GASLLT obj's underlying member method code with example usage: obj.plot_predict_is(h, fit_once, fit_method) without return types