class_docstr: Inherits time series methods from TSM class.
  
  **** 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.
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docstr: Creates forecasted states and variances
  
  Parameters
  ----------
  beta : np.ndarray
      Contains untransformed starting values for latent variables
  
  Returns
  ----------
  a : np.ndarray
      Forecasted states
  
  P : np.ndarray
      Variance of forecasted states
docstr_tok: ["Creates","forecasted","states","and","variances","Parameters","--","--","--","--","--","beta",":","np.ndarray","Contains","untransformed","starting","values","for","latent","variables","Returns","--","--","--","--","--","a",":","np.ndarray","Forecasted","states","P",":","np.ndarray","Variance","of","forecasted","states"]
code: def _forecast_model(self, beta, h):
      """Creates forecasted states and variances
  
      Parameters
      ----------
      beta : np.ndarray
          Contains untransformed starting values for latent variables
  
      Returns
      ----------
      a : np.ndarray
          Forecasted states
  
      P : np.ndarray
          Variance of forecasted states
      """
  
      T, Z, R, Q, H = self._ss_matrices(beta)
      return llt_univariate_kalman_fcst(
          self.data, Z, H, T, Q, R, 0.0, h
      )
  
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 3 for calling the LLT obj's underlying member method code with example usage: obj._forecast_model(beta, h) and returns: llt_univariate_kalman_fcst