'nperiod ahead forecast at the end of the data period
forecast is based on the error estimates'

def forecast(self, ar=None, ma=None, nperiod=10):

    eta = np.r_[(self.error_estimate, np.zeros(nperiod))]
    if (ar is None):
        ar = self.ar_est
    if (ma is None):
        ma = self.ma_est
    return signal.lfilter(ma, ar, eta)
