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 structure of the model (model matrices etc) for mini batch model.
  
  Here the structure is the same as for _normal_model() but we are going to
  sample a random choice of data points (of length mini_batch).
  
  Parameters
  ----------
  beta : np.ndarray
      Contains untransformed starting values for the latent variables
  
  mini_batch : int
      Mini batch size for the data sampling
  
  Returns
  ----------
  mu : np.ndarray
      Contains the predicted values (location) for the time series
  
  Y : np.ndarray
      Contains the length-adjusted time series (accounting for lags)
docstr_tok: ["Creates","the","structure","of","the","model","(","model","matrices","etc",")","for","mini","batch","model",".","Here","the","structure","is","the","same","as","for","_normal_model","(",")","but","we","are","going","to","sample","a","random","choice","of","data","points","(","of","length","mini_batch",")",".","Parameters","--","--","--","--","--","beta",":","np.ndarray","Contains","untransformed","starting","values","for","the","latent","variables","mini_batch",":","int","Mini","batch","size","for","the","data","sampling","Returns","--","--","--","--","--","mu",":","np.ndarray","Contains","the","predicted","values","(","location",")","for","the","time","series","Y",":","np.ndarray","Contains","the","length-adjusted","time","series","(","accounting","for","lags",")"]
code: def _mb_normal_model(self, beta, mini_batch):
      """Creates the structure of the model (model matrices etc) for mini batch model.
  
      Here the structure is the same as for _normal_model() but we are going to
      sample a random choice of data points (of length mini_batch).
  
      Parameters
      ----------
      beta : np.ndarray
          Contains untransformed starting values for the latent variables
  
      mini_batch : int
          Mini batch size for the data sampling
  
      Returns
      ----------
      mu : np.ndarray
          Contains the predicted values (location) for the time series
  
      Y : np.ndarray
          Contains the length-adjusted time series (accounting for lags)
      """
  
      rand_int = np.random.randint(
          low=0, high=self.data.shape[0] - mini_batch - self.max_lag + 1
      )
      sample = np.arange(start=rand_int, stop=rand_int + mini_batch)
  
      Y = self.data[self.max_lag :][sample]
      X = self.X[:, sample]
  
      # Transform latent variables
      z = np.array(
          [
              self.latent_variables.z_list[k].prior.transform(beta[k])
              for k in range(beta.shape[0])
          ]
      )
  
      # Constant and AR terms
      if self.ar != 0:
          mu = np.matmul(
              np.transpose(X), z[: -self.family_z_no - self.ma]
          )
      else:
          mu = np.ones(Y.shape[0]) * z[0]
  
      # MA terms
      if self.ma != 0:
          mu = arima_recursion_normal(
              z, mu, Y, self.max_lag, Y.shape[0], self.ar, self.ma
          )
  
      return mu, Y
  
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 9 for calling the ARIMA obj's underlying member method code with example usage: obj._mb_normal_model(beta, mini_batch) and returns: mu,  Y