LPC Linear prediction coefficients
The Burg-method is used to estimate the prediction coefficients

A = lpc(Y [,P]) finds the coefficients  A=[ 1 A(2) ... A(N+1) ],
of an Pth order forward linear predictor

Xp(n) = -A(2)*X(n-1) - A(3)*X(n-2) - ... - A(N+1)*X(n-P)

such that the sum of the squares of the errors

err(n) = X(n) - Xp(n)

is minimized.  X can be a vector or a matrix.  If X is a matrix
containing a separate signal in each column, LPC returns a model
estimate for each column in the rows of A. N specifies the order
of the polynomial A(z).

If you do not specify a value for P, LPC uses a default P = length(X)-1.


see also ACOVF ACORF AR2POLY RC2AR DURLEV SUMSKIPNAN LATTICE