class_docstr: Inherits time series methods from TSM class.
  
  **** GENERALIZED AUTOREGRESSIVE SCORE LOCAL LEVEL 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: Makes forecast with the estimated model
  
  Parameters
  ----------
  h : int (default : 5)
      How many steps ahead would you like to forecast?
  
  intervals : boolean (default: False)
      Whether to return prediction intervals
  
  Returns
  ----------
  - pd.DataFrame with predicted values
docstr_tok: ["Makes","forecast","with","the","estimated","model","Parameters","--","--","--","--","--","h",":","int","(","default",":","5",")","How","many","steps","ahead","would","you","like","to","forecast","?","intervals",":","boolean","(","default",":","False",")","Whether","to","return","prediction","intervals","Returns","--","--","--","--","--","-","pd.DataFrame","with","predicted","values"]
code: def predict(self, h=5, intervals=False):
      """Makes forecast with the estimated model
  
      Parameters
      ----------
      h : int (default : 5)
          How many steps ahead would you like to forecast?
  
      intervals : boolean (default: False)
          Whether to return prediction intervals
  
      Returns
      ----------
      - pd.DataFrame with predicted values
      """
  
      if self.latent_variables.estimated is False:
          raise Exception("No latent variables estimated!")
      else:
          theta, Y, scores = self._model(
              self.latent_variables.get_z_values()
          )
          date_index = self.shift_dates(h)
  
          if self.latent_variables.estimation_method in ["M-H"]:
              sim_vector = self._sim_prediction_bayes(h, 15000)
  
              forecasted_values = np.array(
                  [np.mean(i) for i in sim_vector]
              )
              prediction_01 = np.array(
                  [np.percentile(i, 1) for i in sim_vector]
              )
              prediction_05 = np.array(
                  [np.percentile(i, 5) for i in sim_vector]
              )
              prediction_95 = np.array(
                  [np.percentile(i, 95) for i in sim_vector]
              )
              prediction_99 = np.array(
                  [np.percentile(i, 99) for i in sim_vector]
              )
  
          else:
              t_z = self.transform_z()
              mean_values = self._mean_prediction(
                  theta, Y, scores, h, t_z
              )
              if intervals is True:
                  sim_values = self._sim_prediction(
                      theta, Y, scores, h, t_z, 15000
                  )
              else:
                  sim_values = self._sim_prediction(
                      theta, Y, scores, h, t_z, 2
                  )
  
              if self.model_name2 == "Skewt":
                  (
                      model_scale,
                      model_shape,
                      model_skewness,
                  ) = self._get_scale_and_shape(t_z)
                  m1 = (
                      np.sqrt(model_shape)
                      * sp.gamma((model_shape - 1.0) / 2.0)
                  ) / (np.sqrt(np.pi) * sp.gamma(model_shape / 2.0))
                  forecasted_values = (
                      mean_values[-h:]
                      + (model_skewness - (1.0 / model_skewness))
                      * model_scale
                      * m1
                  )
              else:
                  forecasted_values = mean_values[-h:]
  
          if intervals is False:
              result = pd.DataFrame(forecasted_values)
              result.rename(columns={0: self.data_name}, inplace=True)
          else:
              # Get mean prediction and simulations (for errors)
              if self.latent_variables.estimation_method not in ["M-H"]:
                  sim_values = self._sim_prediction(
                      theta, Y, scores, h, t_z, 15000
                  )
                  prediction_01 = np.array(
                      [np.percentile(i, 1) for i in sim_values]
                  )
                  prediction_05 = np.array(
                      [np.percentile(i, 5) for i in sim_values]
                  )
                  prediction_95 = np.array(
                      [np.percentile(i, 95) for i in sim_values]
                  )
                  prediction_99 = np.array(
                      [np.percentile(i, 99) for i in sim_values]
                  )
  
              result = pd.DataFrame(
                  [
                      forecasted_values,
                      prediction_01,
                      prediction_05,
                      prediction_95,
                      prediction_99,
                  ]
              ).T
              result.rename(
                  columns={
                      0: self.data_name,
                      1: "1% Prediction Interval",
                      2: "5% Prediction Interval",
                      3: "95% Prediction Interval",
                      4: "99% Prediction Interval",
                  },
                  inplace=True,
              )
  
          result.index = date_index[-h:]
  
          return result
  
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_llev_recursion
label_desc: Use image node_id 22 for calling the GASLLEV obj's underlying member method code with example usage: obj.predict(h, intervals) and returns: result