On Fri, Oct 14, 2011 at 2:29 PM, <josef.p...@gmail.com> wrote: > On Fri, Oct 14, 2011 at 2:18 PM, Alan G Isaac <alan.is...@gmail.com> wrote: >> On 10/14/2011 1:42 PM, josef.p...@gmail.com wrote: >>> If I remember correctly, signal.lfilter doesn't require stationarity, >>> but handling of the starting values is a bit difficult. >> >> >> Hmm. Yes. >> AR(1) is trivial, but how do you handle higher orders? > > I would have to look for it. > You can invert the stationary part of the AR polynomial with the numpy > polynomial classes using division. The main thing is to pad with > enough zeros corresponding to the truncation that you want. And in the > old classes to watch out because the order is reversed high to low > instead of low to high or the other way around.
I found it in the examples folder (pure numpy) https://github.com/statsmodels/statsmodels/blob/master/scikits/statsmodels/examples/tsa/lagpolynomial.py >>> ar = LagPolynomial([1, -0.8]) >>> ma = LagPolynomial([1]) >>> ma.div(ar) Polynomial([ 1. , 0.8], [-1., 1.]) >>> ma.div(ar, maxlag=50) Polynomial([ 1.00000000e+00, 8.00000000e-01, 6.40000000e-01, 5.12000000e-01, 4.09600000e-01, 3.27680000e-01, 2.62144000e-01, 2.09715200e-01, 1.67772160e-01, 1.34217728e-01, 1.07374182e-01, 8.58993459e-02, 6.87194767e-02, 5.49755814e-02, 4.39804651e-02, 3.51843721e-02, 2.81474977e-02, 2.25179981e-02, 1.80143985e-02, 1.44115188e-02, 1.15292150e-02, 9.22337204e-03, 7.37869763e-03, 5.90295810e-03, 4.72236648e-03, 3.77789319e-03, 3.02231455e-03, 2.41785164e-03, 1.93428131e-03, 1.54742505e-03, 1.23794004e-03, 9.90352031e-04, 7.92281625e-04, 6.33825300e-04, 5.07060240e-04, 4.05648192e-04, 3.24518554e-04, 2.59614843e-04, 2.07691874e-04, 1.66153499e-04, 1.32922800e-04, 1.06338240e-04, 8.50705917e-05, 6.80564734e-05, 5.44451787e-05, 4.35561430e-05, 3.48449144e-05, 2.78759315e-05, 2.23007452e-05], [-1., 1.]) >>> ar = LagPolynomial([1, -0.8, 0.2, 0.1, 0.1]) >>> ma.div(ar, maxlag=50) Polynomial([ 1.00000000e+00, 8.00000000e-01, 4.40000000e-01, 9.20000000e-02, -1.94400000e-01, -2.97920000e-01, -2.52656000e-01, -1.32300800e-01, -6.07744000e-03, 7.66558080e-02, 1.01035814e-01, 7.93353139e-02, 3.62032515e-02, -4.67362386e-03, -2.90166622e-02, -3.38324615e-02, -2.44155995e-02, -9.39695872e-03, 3.65046531e-03, 1.06245701e-02, 1.11508188e-02, 7.37039040e-03, 2.23864501e-03, -1.86070097e-03, -3.78841070e-03, -3.61949191e-03, -2.17570579e-03, -4.51755084e-04, 8.14527351e-04, 1.32149267e-03, 1.15703475e-03, 6.25052041e-04, 5.50326804e-05, -3.28837006e-04, -4.52284820e-04, -3.64068927e-04, -1.73417745e-04, 1.21917720e-05, 1.26072341e-04, 1.52168186e-04, 1.12642678e-04, 4.58540937e-05, -1.36693133e-05, -4.65873557e-05, -5.03856990e-05, -3.42095661e-05], [-1., 1.]) no guarantees, I don't remember how much I tested these things, but I spent a lot of time doing it 3 or 4 different ways. Josef > > I switched to using mostly lfilter, but I guess the statsmodels > sandbox (and the mailing list) still has my "playing with ARMA > polynomials" code. > (I think it might be pretty useful for teaching. I wished I had the > functions to calculate some examples when I learned this.) > > Josef >> >> Thanks, >> Alan >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
