class_docstr: Inherits time series methods from TSM class.
  
  **** GAUSSIAN PROCESS NONLINEAR AUTOREGRESSIVE (GP-NARX) MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the time series data that will be used.
  
  ar : int
      Field to specify how many AR terms the model will have.
  
  kernel : kernel object
      For example, SquaredExponential() or OrnsteinUhlenbeck()
  
  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.
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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
  
  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","Returns","--","--","--","--","--","-","Plot","of","the","forecast","against","data"]
code: def plot_predict_is(self, h=5, fit_once=True, **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
  
      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_once=fit_once)
      data = self.data[-h:]
  
      plt.plot(
          date_index,
          data * self._norm_std + self._norm_mean,
          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.linalg
  import scipy.sparse
  import scipy.stats
  from scipy.stats import multivariate_normal
  from ..None import arma
  from ..None import output
  from ..None import tests
  from ..None import tsm
  from ..None import data_check
  from .kernels import *
label_desc: Use image node_id 13 for calling the GPNARX obj's underlying member method code with example usage: obj.plot_predict_is(h, fit_once) without return types