class_docstr: Inherits time series methods from TSM class.
  
  **** GENERALIZED AUTOREGRESSIVE SCORE LOCAL LINEAR TREND MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the univariate 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.
  
  dist : str
      Which distribution to use
docstr: Preoptimizes the model by estimating a static model, then a quick search of good AR/SC parameters
  
  Parameters
  ----------
  initials : np.array
      A vector of inital values
  
  method : str
      One of 'MLE' or 'PML' (the optimization options)
  
  Returns
  ----------
  Y_exp : np.array
      Vector of past values and predictions 
docstr_tok: ["Preoptimizes","the","model","by","estimating","a","static","model",",","then","a","quick","search","of","good","AR\/SC","parameters","Parameters","--","--","--","--","--","initials",":","np.array","A","vector","of","inital","values","method",":","str","One","of","'MLE","'","or","'PML","'","(","the","optimization","options",")","Returns","--","--","--","--","--","Y_exp",":","np.array","Vector","of","past","values","and","predictions"]
code: def _preoptimize_model(self, initials, method):
      """Preoptimizes the model by estimating a static model, then a quick search of good AR/SC parameters
  
      Parameters
      ----------
      initials : np.array
          A vector of inital values
  
      method : str
          One of 'MLE' or 'PML' (the optimization options)
  
      Returns
      ----------
      Y_exp : np.array
          Vector of past values and predictions
      """
  
      random_starts = np.random.normal(0.1, 0.1, [2, 1000])
  
      best_start = self.latent_variables.get_z_starting_values()
      best_lik = self.neg_loglik(
          self.latent_variables.get_z_starting_values()
      )
      proposal_start = best_start.copy()
  
      for start in range(random_starts.shape[1]):
          proposal_start[0:2] = random_starts[:, start]
          proposal_likelihood = self.neg_loglik(proposal_start)
          if proposal_likelihood < best_lik:
              best_lik = proposal_likelihood
              best_start = proposal_start.copy()
      return best_start
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  import scipy.special
  from ..None import families
  from ..None import tsm
  from ..None import data_check
  from .gas_core_recursions import gas_llt_recursion
label_desc: Use image node_id 11 for calling the GASLLT obj's underlying member method code with example usage: obj._preoptimize_model(initials, method) and returns: best_start