We will consider applications to data formed from measurements
sampled at equal time intervals, TAU, of n_{x} input, state and output
variables. Here we will not distinguish among these types of variables.
The data point x[i,j] is the measurement of variable i taken at
time step j. In general, each variable may depend upon previous
measurements of itself and the other variables, except for input
variables which do not depend upon state and output variables.
A type of ARMA model applied to such systems is the vector autoregressive
(VAR) model, in which a prediction is found by a linear combination
of previous (lagged) measurements, x[i,jl]:
[1]
where 1 ³ i ³ nx, 1 ³ kp ³ nx; kp ¹ i if
lp = 0; lm+1 ³ j ³ nd; lm ³ 1 is the maximum lag;
and e[i,j] is the error in the model prediction. The parameters
a are determined by fitting the model to a set of data. An identification
process is used to select which of the possible terms in equation
1 contribute significantly to the model, and only those terms are
retained.
More complex behaviors, such as coupled sensitivities between variables
or curvature in the responses, could be included in an VAR model
by adding polynomial terms to equation 2:
[4a]
[4b]
The additional parameters of this model compared to equation 1 are
bp,m, the (usually positive integer) exponents for each multiplicand
in each term, and nm is the maximum number of multiplicands in each
term of the model. The indices i, j, k, and l are defined as for
equation 1.
The model is made tractable by restricting the values that can
be taken on by the exponents, b, the lags, and the value of nm,
and by including in the model only those terms which contribute
significantly to the fit. The fitting procedure involves a stepwise
selection process, described below, in which a set of candidate
terms are tested for inclusion in the model.

The restricted set of candidate terms are formed as follows: First,
a list of n_{e} candidate exponents is selected, not including zero
which is always assumed. Then, a list of lags to be considered is
formed. Formation of this list may be an iterative process involving
sequentially adding lags until the model cannot be improved. In
some cases discontinuous lags may be added to the list to represent
expected seasonal effects. If lag 0 is included in the list, then
variable i is being correlated to "current" values of
the other variables, and variable i, lag 0 must be excluded from
the candidate terms. The total number of lags in the list, which
may include lag zero, is n_{l}.
The stepwise procedure then selects a set of polynomial terms from
the candidates that optimizes the fitting criteria. The resulting
MPR model can thus be completely specified by a table containing
the following information for each term:
kp,1, lp,1, b1; kp,2, lp,2, b2; . . . kp,nm, lp,nm, bnm; ap
The Number of Candidate Terms
Adding lagged values increases the number of "independent variables."
The total number of independent variables, n_{v} will be:
[6]
where l_{0} equals one if the list of lags includes zero, and equals
zero otherwise, and v_{0} equals one if lagged dependent variables
are included as independent variables, and zero otherwise.
The maximum value that nm can take is n_{v}. If n_{m} = n_{v}, then the total
number of candidate terms is:
[5]
This may result in a large number of terms to be tested for selection
into the model. For example, if there are three independent variables
(n_{x}=4) and no lags (n_{l}=1), and ten exponents (n_{e}=10), then there
are n_{t} = 1331 possible terms. Experience with a wide variety of
datasets has shown that n_{m} can often be restricted to two or three.
For n_{m} £ n_{v}:
[7]
where C(n_{v, m}) is the number of combinations of n_{v} objects taken
m at a time. For the example above, if n_{m} = 2, the number of candidate
terms drops to 331.
