[R] Fitting ARMA model with known inputs.

[Rolf Turner]

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 at 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.