class_docstr: Inherits time series methods from TSM parent class.
  
  **** skew BETA-t-EGARCH IN MEAN MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the univariate time series data that will be used.
  
  p : int
      Field to specify how many GARCH terms the model will have.
  
  q : int
      Field to specify how many SCORE terms the model will have.
  
  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.
class_docstr_tok: ["Inherits","time","series","methods","from","TSM","parent","class",".","*","*","*","*","skew","BETA-t-EGARCH","IN","MEAN","MODELS","*","*","*","*","Parameters","--","--","--","--","--","data",":","pd.DataFrame","or","np.array","Field","to","specify","the","univariate","time","series","data","that","will","be","used",".","p",":","int","Field","to","specify","how","many","GARCH","terms","the","model","will","have",".","q",":","int","Field","to","specify","how","many","SCORE","terms","the","model","will","have",".","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","."]
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:
          sigma2, Y, scores, theta = 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)
              error_bars = []
  
              for pre in range(5, 100, 5):
                  error_bars.append(
                      np.insert(
                          [np.percentile(i, pre) for i in sim_vector],
                          0,
                          sigma2[-1],
                      )
                  )
  
              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()
  
              if intervals is True:
                  sim_values = self._sim_prediction(
                      sigma2, Y, scores, h, t_z, 15000
                  )
              else:
                  sim_values = self._sim_prediction(
                      sigma2, Y, scores, h, t_z, 2
                  )
  
              mean_values = self._sim_predicted_mean(
                  sigma2, Y, scores, h, t_z, 15000
              )
              forecasted_values = mean_values[-h:]
  
          if intervals is False:
              result = pd.DataFrame(np.exp(forecasted_values / 2.0))
              result.rename(columns={0: self.data_name}, inplace=True)
          else:
              if self.latent_variables.estimation_method not in ["M-H"]:
                  sim_values = self._sim_prediction(
                      sigma2, 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 = np.exp(
                  pd.DataFrame(
                      [
                          forecasted_values,
                          prediction_01,
                          prediction_05,
                          prediction_95,
                          prediction_99,
                      ]
                  ).T
                  / 2.0
              )
              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 output
  from ..None import tests
  from ..None import tsm
  from ..None import gas
  from ..None import data_check
label_desc: Use image node_id 17 for calling the SEGARCHM obj's underlying member method code with example usage: obj.predict(h, intervals) and returns: result