class_docstr: Inherits time series methods from TSM class.
  
  **** NON-GAUSSIAN 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.
  
  family : 
      e.g. pf.Normal(0,1)
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code: def __init__(self, data, family, integ=0, target=None):
      # Initialize TSM object
      super(NLLT, self).__init__("NLLT")
  
      # Latent Variables
      self.integ = integ
      self.target = target
      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.multivariate_model = False
      self.state_no = 2
  
      # 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
      X = self.data
      for order in range(self.integ):
          X = np.diff(X)
          self.data_name = "Differenced " + self.data_name
      self.data = X
      self.cutoff = 0
      self.data_length = self.data.shape[0]
  
      self._create_latent_variables()
      self.family = family
      (
          self.model_name2,
          self.link,
          self.scale,
          self.shape,
          self.skewness,
          self.mean_transform,
          self.cythonized,
      ) = self.family.setup()
  
      self.model_name = self.model_name2 + " Local Linear Trend Model"
  
      # Build any remaining latent variables that are specific to the family chosen
      for no, i in enumerate(self.family.build_latent_variables()):
          self.latent_variables.add_z(i[0], i[1], i[2])
          self.latent_variables.z_list[no + 1].start = i[3]
  
      self.z_no = len(self.latent_variables.z_list)
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  from scipy import optimize
  from ..None import inference
  from ..None import families
  from ..None import output
  from ..None import tsm
  from ..None import data_check
  from ..None import covariances
  from ..None import results
  from ..None import gas
  from .kalman import *
  from .llt import *
label_desc: Use image node_id 1 to create a new NLLT object from inherited base classes: tsm with example: obj = NLLT(data, family, integ, target)