Multidimensional autoregression

TIME-SERIES AUTOREGRESSION

Given $y_t$ and $y_{t-1}$ , you might like to predict $y_{t+1}$ . The prediction could be a scaled sum or difference of $y_t$ and $y_{t-1}$ . This is called ``autoregression'' because a signal is regressed on itself. To find the scale factors you would optimize the fitting goal below, for the prediction filter $(f_1,f_2)$ :

$$\bold 0 \quad \approx \quad \bold r \eq \left[ \begin{array}{ccc}... ... - \left[ \begin{array}{c} y_2 y_3 y_4 y_5 y_6 \end{array} \right]$$ (9)

(In practice, of course the system of equations would be much taller, and perhaps somewhat wider.) A typical row in the matrix (6.9) says that $y_{t+1} \approx y_t f_1 + y_{t-1} f_2$ hence the description of $f$ as a ``prediction'' filter. The error in the prediction is simply the residual. Define the residual to have opposite polarity and merge the column vector into the matrix, so you get

$$\left[ \begin{array}{c} 0 0 0 0 0 \end{array} \right]... ...array} \right] \; \left[ \begin{array}{c} 1 -f_1 -f_2 \end{array} \right]$$ (10)

which is a standard form for autoregressions and prediction error.

Multiple reflections are predictable. It is the unpredictable part of a signal, the prediction residual, that contains the primary information. The output of the filter $(1,-f_1, -f_2) = (a_0, a_1, a_2)$ is the unpredictable part of the input. This filter is a simple example of a ``prediction-error'' (PE) filter. It is one member of a family of filters called ``error filters.''

The error-filter family are filters with one coefficient constrained to be unity and various other coefficients constrained to be zero. Otherwise, the filter coefficients are chosen to have minimum power output. Names for various error filters follow:

$(1, a_1,a_2,a_3, \cdots ,a_n)$	prediction-error (PE) filter
$(1, 0, 0, a_3,a_4,\cdots ,a_n)$	gapped PE filter with a gap
$(a_{-m}, \cdots, a_{-2}, a_{-1}, 1, a_1, a_2, a_3, \cdots ,a_n)$	interpolation-error (IE) filter

We introduce a free-mask matrix $\bold K$ which ``passes'' the freely variable coefficients in the filter and ``rejects'' the constrained coefficients (which in this first example is merely the first coefficient $a_0=1$ ).

$$\bold K \eq \left[ \begin{array}{cccccc} 0 & . & . . & 1 & . . & . & 1 \end{array} \right]$$ (11)

To compute a simple prediction error filter $\bold a =(1, a_1, a_2)$ with the CD method, we write (6.9) or (6.10) as

$$\bold 0 \quad \approx \quad \bold r \eq \left[ \begin{array}{ccc}... ... + \left[ \begin{array}{c} y_2 y_3 y_4 y_5 y_6 \end{array} \right]$$ (12)

Let us move from this specific fitting goal to the general case. (Notice the similarity of the free-mask matrix $\bold K$ in this filter estimation problem with the free-mask matrix $\bold J$ in missing data goal ().) The fitting goal is,

$$\bold 0 \approx \bold Y \bold a$$ (13)

$$\bold 0 \approx \bold Y(\bold I-\bold K+\bold K) \bold a$$ (14)

$$\bold 0 \approx \bold Y\bold K\bold a +\bold Y(\bold I-\bold K)\bold a$$ (15)

$$\bold 0 \approx \bold Y\bold K\bold a +\bold Y \bold a_0$$ (16)

$$\bold 0 \approx \bold Y\bold K\bold a +\bold y$$ (17)

$$\bold 0 \quad\approx\quad \bold r = \bold Y\bold K\bold a +\bold r_0$$ (18)

which means we initialize the residual with $\bold r_0 = \bold y$ . and then iterate with

$$\Delta \bold a \longleftarrow \bold K' \bold Y' \bold r$$ (19)

$$\Delta \bold r \longleftarrow \bold Y \bold K \Delta \bold a$$ (20)

Multidimensional autoregression