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.