Simulates a h-step ahead mean prediction

        Parameters
        ----------
        mu : np.ndarray
            The past predicted values

        Y : np.ndarray
            The past data

        h : int
            How many steps ahead for the prediction

        t_z : np.ndarray
            A vector of (transformed) latent variables

        simulations : int
            How many simulations to perform

        Returns
        ----------
        Matrix of simulations

def _sim_prediction(self, mu, Y, h, t_z, simulations):
        """ Simulates a h-step ahead mean prediction

        Parameters
        ----------
        mu : np.ndarray
            The past predicted values

        Y : np.ndarray
            The past data

        h : int
            How many steps ahead for the prediction

        t_z : np.ndarray
            A vector of (transformed) latent variables

        simulations : int
            How many simulations to perform

        Returns
        ----------
        Matrix of simulations
        """     

        model_scale, model_shape, model_skewness = self._get_scale_and_shape(t_z)
        sim_vector = np.zeros([simulations,h])

        for n in range(0, simulations):
            # Create arrays to iteratre over        
            Y_exp = Y.copy()

            # Loop over h time periods          
            for t in range(0,h):

                if self.ar != 0:
                    Y_exp_normalized = (Y_exp[-self.ar:][::-1] - self._norm_mean) / self._norm_std
                    new_value = self.predict_new(np.append(1.0, Y_exp_normalized), self.latent_variables.get_z_values())

                else:
                    new_value = self.predict_new(np.array([1.0]), self.latent_variables.get_z_values())

                new_value += np.random.randn(1)*t_z[-1]

                if self.model_name2 == "Exponential":
                    rnd_value = self.family.draw_variable(1.0/self.link(new_value), model_scale, model_shape, model_skewness, 1)[0]
                else:
                    rnd_value = self.family.draw_variable(self.link(new_value), model_scale, model_shape, model_skewness, 1)[0]

                Y_exp = np.append(Y_exp, [rnd_value])

            sim_vector[n] = Y_exp[-h:]

        return np.transpose(sim_vector)