def forecast2(self, step_ahead=1, start=None, end=None, endog=None):
        if step_ahead != 1:
            raise NotImplementedError

        p,q = self.nar, self.nma
        k = 0
        errors = self.error_estimate
        y = self.endog

        #this is for 1step ahead only, still need h-step predictive polynomial
        arcoefs_rev = self.params[k:k+p][::-1]
        macoefs_rev = self.params[k+p:k+p+q][::-1]


        predicted = []
        # create error vector iteratively
        for i in range(start, end):
            predicted.append(sum(arcoefs_rev*y[i-p:i]) + sum(macoefs_rev * errors[i-p:i]))

        return np.asarray(predicted)
Create a batch-request only using the Communication-object
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/tsa/arma_mle.py/Arma.forecast2--crossover with--dataset/ETHPy150Open Zimbra-Community/python-zimbra/tests/test_genrequest.py/TestGenrequest.test_genrequest_batch