From: Rolf Turner <rolf_at_math.unb.ca>

Date: Thu 02 Jun 2005 - 01:24:55 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Thu Jun 02 01:52:29 2005

Date: Thu 02 Jun 2005 - 01:24:55 EST

It is not at all clear what you want to do. One conjecture
(attempt at reading your mind):

X_t = ``black box's state'' at time t f_t = ``force'' at time t Proposed model e.g. AR(3): X_t = phi_1 * X_{t-1} + phi_2 * X_{t-2} + phi_3 * X_{t-3} + f_t You wish to identify/estimate the coefficients phi_1, phi_2, phi_3.

Remarks:

(a) This model probably doesn't make a lot of sense, with known/observed f_t. It will almost surely not hold exactly, for ***any*** values of the phi_i. (b) A model which makes a bit more sense, in the abstract, is X_t = phi_1 * X_{t-1} + phi_2 * X_{t-2} + phi_3 * X_{t-3} + f_t + E_t where E_t is (unobserved) i.i.d. random ``error''. (c) This last model is just a simple regression model and may be fitted using lm(). cheers, Rolf Turner rolf@math.unb.ca

Original message:

> Hello!

*> Is it possible to use R time series to identificate a process which is
**> subjected to known input? I.e. I have 2 sequences - one is measurements
**> of black box's state and the second is the "force" by which this black
**> box is driven (which is known too) and I want to fit thist two series
**> with AR-process. The "ar" procedure from stats package expects that the
**> force is always random. Is it possible to feed it known vector as input
**> parameter?
**> Thank you in advance.
*

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Thu Jun 02 01:52:29 2005

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