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: Calculates the negative log-likelihood of the model for Normal family
  
  Parameters
  ----------
  beta : np.ndarray
      Contains untransformed starting values for latent variables
  
  Returns
  ----------
  The negative logliklihood of the model
docstr_tok: ["Calculates","the","negative","log-likelihood","of","the","model","for","Normal","family","Parameters","--","--","--","--","--","beta",":","np.ndarray","Contains","untransformed","starting","values","for","latent","variables","Returns","--","--","--","--","--","The","negative","logliklihood","of","the","model"]
code: def normal_neg_loglik(self, beta):
      """Calculates the negative log-likelihood of the model for Normal family
  
      Parameters
      ----------
      beta : np.ndarray
          Contains untransformed starting values for latent variables
  
      Returns
      ----------
      The negative logliklihood of the model
      """
  
      mu, Y = self._model(beta)
      return -np.sum(
          ss.norm.logpdf(
              Y,
              loc=mu,
              scale=self.latent_variables.z_list[-1].prior.transform(
                  beta[-1]
              ),
          )
      )
  
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 16 for calling the ARIMA obj's underlying member method code with example usage: obj.normal_neg_loglik(beta) and returns: unknown