How to Improve the computation speed for 'ARMAX' function?

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Amelia Lita
Amelia Lita 2020-6-2
编辑： Rajiv Singh 2020-6-9
Hi, I'm currently using ARMAX function to compute a model. I would like to compute all possible model at once in the code (3000 models at once) but I guess it's too much and the computing time took so long. Is there a way to improve my computation speed? Here's the code for the armax function that I used.
na = 1:10; %polinom output
nb = 1:10; %polinom input
nc = 1:5; %polinom noise
nk = 0:5;
order = struc(na,nb,nc,nk);
models = cell(size(order,1),1);
for od = 1:size(order,1) %ARIMAX MODEL
models{od} = armax(dataest,[order(od,:)],'IntegrateNoise', true);
end
Please help me, to improve my computation speed since I'm still at lost on how to improve the speed through efficiency of my code. Thank you!

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回答（1 个）

Rajiv Singh
Rajiv Singh 2020-6-9
编辑：Rajiv Singh 2020-6-9
If you have access to Parallel Computing Toolbox, you could consider replacing the for-loop with a "parfor" loop. Other things to consider:
  • If parameter uncertainty is not needed, you can turn it off using armaxOptions:EstimateCovariance flag.
  • Try reducing armaxOptions:SearchOptions:MaximumIterations.
  • Try a different estimation technique, like n4sid which is faster. You can convert the resulting model into polynomial (armax) form using IDPOLY() command. Example:
sys = idpoly(n4sid(data, 2));
But this will be hard to do for ARIMAX case. For that, you can replace data with diff(data) and fit a regular model (no noise integrators) to it. Example:
data2 = diff(data); % remove integration effects from data
sys = idpoly(n4sid(data2,2));
sys.D = [1 -1] % add the integrator back

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