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 Autoregressive matrix for the model
  
  Returns
  ----------
  X : np.ndarray
      Autoregressive Matrix
docstr_tok: ["Creates","the","Autoregressive","matrix","for","the","model","Returns","--","--","--","--","--","X",":","np.ndarray","Autoregressive","Matrix"]
code: def _ar_matrix(self):
      """Creates the Autoregressive matrix for the model
  
      Returns
      ----------
      X : np.ndarray
          Autoregressive Matrix
      """
  
      X = np.ones(self.data_length - self.max_lag)
  
      if self.ar != 0:
          for i in range(0, self.ar):
              X = np.vstack(
                  (X, self.data[(self.max_lag - i - 1) : -i - 1])
              )
  
      return X
  
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 2 for calling the ARIMA obj's underlying member method code with example usage: obj._ar_matrix() and returns: X