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: Creates the state space matrices required
  
  Parameters
  ----------
  beta : np.array
      Contains untransformed starting values for latent_variables
  
  Returns
  ----------
  T, Z, R, Q, H : np.array
      State space matrices used in KFS algorithm
docstr_tok: ["Creates","the","state","space","matrices","required","Parameters","--","--","--","--","--","beta",":","np.array","Contains","untransformed","starting","values","for","latent_variables","Returns","--","--","--","--","--","T",",","Z",",","R",",","Q",",","H",":","np.array","State","space","matrices","used","in","KFS","algorithm"]
code: def _ss_matrices(self, beta):
      """Creates the state space matrices required
  
      Parameters
      ----------
      beta : np.array
          Contains untransformed starting values for latent_variables
  
      Returns
      ----------
      T, Z, R, Q, H : np.array
          State space matrices used in KFS algorithm
      """
  
      T = np.identity(1)
      R = np.identity(1)
      Z = np.identity(1)
      H = np.identity(1) * self.latent_variables.z_list[
          0
      ].prior.transform(beta[0])
      Q = np.identity(1) * self.latent_variables.z_list[
          1
      ].prior.transform(beta[1])
  
      return T, Z, R, Q, 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 5 for calling the LLEV obj's underlying member method code with example usage: obj._ss_matrices(beta) and returns: T,  Z,  R,  Q,  H