I am new to the topic of system identification and looking for a large Autoregressive (AR) model. Can anybody point out a large stable AR model which has more than 2 coefficients AND there should be a large number of zero coefficients?
This link presents an example of how to have nonzero coefficients in lag 1,4 for order, $p=4$ AR(4) model. But, the values are NaN !! So, how do I apply this technique so as to generate a large AR model with more number of zero coefficients and lesser number of non-zero coefficients ?
Looking for any stable AR($p$) model (preferably popular cited ones, if available) of higher order $p \ge 20$ where the number of non-zero coefficients are few (constraint) . If I generate a noisy sinusoidal wave given below, then
%Generate sine wave = A*sin(2*pi*f*t + phi) t = linspace(0,1,1000); A = 5; f = 2; phi = pi/8; sinewave = A*sin(2*pi*f*t + phi); noisy_sine=sinewave+0.5*randn(size(t)); subplot(1,2,1); plot(t, sinewave) hold on; subplot(1,2,2); plot(t,noisy_sine); %Generate AR model order =20; ARCoeff = aryule(noisy_sine,order);
It returns AR model of 20 lags. But, all the coefficients are non-zero. How do I generate AR model of any order higher than 2 where the number of non-zero coefficients are lesser than the number of zero coefficients ?