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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code: def __init__(self, data, integ=0, target=None):
      # Initialize TSM object
      super(LLEV, self).__init__("LLEV")
  
      # Latent Variables
      self.integ = integ
      self.param_no = 2
      self.max_lag = 0
      self._z_hide = (
          0  # Whether to cutoff variance latent variables from results
      )
      self.supported_methods = ["MLE", "PML", "Laplace", "M-H", "BBVI"]
      self.default_method = "MLE"
      self.model_name = "LLEV"
      self.multivariate_model = False
  
      # Format the data
      (
          self.data,
          self.data_name,
          self.is_pandas,
          self.index,
      ) = dc.data_check(data, target)
      self.data = self.data.astype(np.float)
      self.data_original = self.data
  
      # Difference data
      for order in range(self.integ):
          self.data = np.diff(self.data)
          self.data_name = "Differenced " + self.data_name
  
      self.data_length = self.data.shape[0]
      self._create_latent_variables()
      self.z_no = 2
  
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 1 to create a new LLEV object from inherited base classes: tsm with example: obj = LLEV(data, integ, target)