class_docstr: Inherits time series methods from the TSM parent class.
  
  **** AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.ndarray
      Field to specify the univariate time series data that will be used.
  
  ar : int
      Field to specify how many AR lags the model will have.
  
  ma : int
      Field to specify how many MA lags the model will have.
  
  integ : int (default : 0)
      Specifies how many times to difference the time series.
  
  target : str (if data is a pd.DataFrame) or int (if data is a np.ndarray)
      Specifies which column name or array index to use. By default, first
      column/array index will be selected as the dependent variable.
  
  family : family object
      E.g pf.Normal(), pf.t(), pf.Laplace()...
docstr: Creates the model's latent variables
  
  A latent variable addition requires:
  
  - Latent variable name - e.g Constant
  - Latent variable prior family - e.g. Normal(0, 1)
  - Variational approximation - e.g. Normal(0, 1)
  
  Returns
  ----------
  None (adds to self.latent_variables)
docstr_tok: ["Creates","the","model","'s","latent","variables","A","latent","variable","addition","requires",":","-","Latent","variable","name","-","e.g","Constant","-","Latent","variable","prior","family","-","e.g",".","Normal","(","0",",","1",")","-","Variational","approximation","-","e.g",".","Normal","(","0",",","1",")","Returns","--","--","--","--","--","None","(","adds","to","self.latent_variables",")"]
code: def _create_latent_variables(self):
      """Creates the model's latent variables
  
      A latent variable addition requires:
  
      - Latent variable name - e.g Constant
      - Latent variable prior family - e.g. Normal(0, 1)
      - Variational approximation - e.g. Normal(0, 1)
  
      Returns
      ----------
      None (adds to self.latent_variables)
      """
  
      self.latent_variables.add_z(
          "Constant", fam.Normal(0, 3, transform=None), fam.Normal(0, 3)
      )
  
      for ar_term in range(self.ar):
          self.latent_variables.add_z(
              "AR(" + str(ar_term + 1) + ")",
              fam.Normal(0, 0.5, transform=None),
              fam.Normal(0, 3),
          )
  
      for ma_term in range(self.ma):
          self.latent_variables.add_z(
              "MA(" + str(ma_term + 1) + ")",
              fam.Normal(0, 0.5, transform=None),
              fam.Normal(0, 3),
          )
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  import scipy.special
  from ..None import families
  from ..None import output
  from ..None import tests
  from ..None import tsm
  from ..None import data_check
  from .arma_recursions import arima_recursion, arima_recursion_normal, arima_recursion_poisson
label_desc: Use image node_id 3 for calling the ARIMA obj's underlying member method code with example usage: obj._create_latent_variables() without return types