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.
class_docstr_tok: ["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","."]
docstr: Creates the negative log marginal likelihood of the model
  
  Parameters
  ----------
  
  data : np.array
      Data to be smoothed
  
  beta : np.array
      Contains untransformed starting values for latent variables
  
  Returns
  ----------
  - Smoothed states
docstr_tok: ["Creates","the","negative","log","marginal","likelihood","of","the","model","Parameters","--","--","--","--","--","data",":","np.array","Data","to","be","smoothed","beta",":","np.array","Contains","untransformed","starting","values","for","latent","variables","Returns","--","--","--","--","--","-","Smoothed","states"]
code: def smoothed_state(self, data, beta):
      """Creates the negative log marginal likelihood of the model
  
      Parameters
      ----------
  
      data : np.array
          Data to be smoothed
  
      beta : np.array
          Contains untransformed starting values for latent variables
  
      Returns
      ----------
      - Smoothed states
      """
  
      T, Z, R, Q, H = self._ss_matrices(beta)
      alpha, V = llt_univariate_KFS(data, Z, H, T, Q, R, 0.0)
      return alpha, V
  
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 14 for calling the LLT obj's underlying member method code with example usage: obj.smoothed_state(data, beta) and returns: alpha,  V