class_docstr: Inherits time series methods from TSM class.
  
  **** LOCAL LEVEL 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.
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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
  
  fit_method : string
      Which method to fit the model with
  
  intervals : boolean
      Whether to output prediction intervals or not
  
  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","fit_method",":","string","Which","method","to","fit","the","model","with","intervals",":","boolean","Whether","to","output","prediction","intervals","or","not","Returns","--","--","--","--","--","-","pd.DataFrame","with","predicted","values"]
code: def predict_is(
      self, h=5, fit_once=True, fit_method="MLE", intervals=False
  ):
      """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
  
      fit_method : string
          Which method to fit the model with
  
      intervals : boolean
          Whether to output prediction intervals or not
  
      Returns
      ----------
      - pd.DataFrame with predicted values
      """
  
      predictions = []
  
      for t in range(0, h):
          x = LLEV(
              integ=self.integ, data=self.data_original[: (-h + t)]
          )
          if fit_once is False:
              x.fit(fit_method=fit_method, printer=False)
          if t == 0:
              if fit_once is True:
                  x.fit(fit_method=fit_method, printer=False)
                  saved_lvs = x.latent_variables
              predictions = x.predict(1, intervals=intervals)
          else:
              if fit_once is True:
                  x.latent_variables = saved_lvs
              predictions = pd.concat(
                  [predictions, x.predict(1, intervals=intervals)]
              )
  
      predictions.rename(columns={0: self.data_name}, inplace=True)
      predictions.index = self.index[-h:]
  
      return predictions
  
imports: import copy
  import sys
  import numpy
  import pandas
  import scipy.stats
  from ..None import families
  from ..None import tsm
  from ..None import data_check
  from .kalman import *
label_desc: Use image node_id 11 for calling the LLEV obj's underlying member method code with example usage: obj.predict_is(h, fit_once, fit_method, intervals) and returns: predictions