'Updates the moments of the time t filtering distribution to the
moments of the predictive distribution, which becomes the time
t+1 prior'

def filtered_to_forecast(self):

    (A, C) = (self.ss.A, self.ss.C)
    Q = np.dot(C, C.T)
    self.x_hat = dot(A, self.x_hat)
    self.Sigma = (dot(A, dot(self.Sigma, A.T)) + Q)
