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multivariate analysis

i have 10k data points[multiple inputs and outputs.] what methods are available to fit these data points? shall i go for subspace based methods?
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sarika1977
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sarika1977
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prashant_n_mhatreCommented:
You said you have 10000 data points. If your system is linear 500-700 data points are enough. First plot the data...look at trends...select proper data channel which you think captures the dynamic part.

Check if the data is linear.

Linear:

AR (Auto Regressive), ARX(AutoRegressive eXogeneous), ARMAX(AutoRegressive Moving Average eXogeneous), Box-Jenkins(BJ) models are available. There is not much difference among them if you don't bother about the modeling of residuals. You may go with ARX.


Nonlinear:

NARX (Nonlinear ARX), NARMAX (Nonlinear - ARMAX) and so many options are available. But I would suggest you to try out linear model. If it doesn't fit, you may try out bilinear combinations. Other high order models may not be stable.


Subspace Based Methods:

You may try out PLS (Partial Least Squares), PCA  (Principle Component Analysis), N4SID, CVA etc. They definitely give better fit than others but the order of model will blow up. Ultimately it will result in large computation time.


Feel free to get back to me if something is not clear to you..or need explanation or more info on specific topic
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sarika1977Author Commented:
pls suggest references on n4sid
thanx
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prashant_n_mhatreCommented:
I guess you are using MATLAB. see

>> help n4sid

I have some documents. Send me mail@

prashantm@www.com

I'll send you.
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sarika1977Author Commented:
nice documents
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