class_docstr: Inherits time series methods from TSM class.
  
  **** NON-GAUSSIAN LOCAL LINEAR TREND MODEL ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the 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.
  
  family : 
      e.g. pf.Normal(0,1)
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docstr: Makes dynamic in-sample predictions with the estimated model
  
  Parameters
  ----------
  h : int (default : 5)
      How many steps would you like to forecast?
  
  fit_once : boolean
      (default: True) Fits only once before the in-sample prediction; if False, fits after every new datapoint
      This method is not functional currently for this model
  
  Returns
  ----------
  - pd.DataFrame with predicted values
docstr_tok: ["Makes","dynamic","in-sample","predictions","with","the","estimated","model","Parameters","--","--","--","--","--","h",":","int","(","default",":","5",")","How","many","steps","would","you","like","to","forecast","?","fit_once",":","boolean","(","default",":","True",")","Fits","only","once","before","the","in-sample","prediction",";","if","False",",","fits","after","every","new","datapoint","This","method","is","not","functional","currently","for","this","model","Returns","--","--","--","--","--","-","pd.DataFrame","with","predicted","values"]
code: def predict_is(self, h=5, fit_once=True):
      """Makes dynamic in-sample predictions with the estimated model
  
      Parameters
      ----------
      h : int (default : 5)
          How many steps would you like to forecast?
  
      fit_once : boolean
          (default: True) Fits only once before the in-sample prediction; if False, fits after every new datapoint
          This method is not functional currently for this model
  
      Returns
      ----------
      - pd.DataFrame with predicted values
      """
  
      predictions = []
  
      for t in range(0, h):
          x = NLLT(
              family=self.family,
              integ=self.integ,
              data=self.data_original[: (-h + t)],
          )
  
          x.fit(print_progress=False)
  
          if t == 0:
              predictions = x.predict(h=1)
          else:
              predictions = pd.concat([predictions, x.predict(h=1)])
  
      predictions.rename(columns={0: self.data_name}, inplace=True)
      predictions.index = self.index[-h:]
  
      return predictions
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  from scipy import optimize
  from ..None import inference
  from ..None import families
  from ..None import output
  from ..None import tsm
  from ..None import data_check
  from ..None import covariances
  from ..None import results
  from ..None import gas
  from .kalman import *
  from .llt import *
label_desc: Use image node_id 23 for calling the NLLT obj's underlying member method code with example usage: obj.predict_is(h, fit_once) and returns: predictions