lpc

 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