A new algorithm for adaptive prediction error filter is presented. It takes the difference between adjacent data as the input rather than the data itself. A modified time domain orthogonal algorithm is used for updating the coefficients. Computer simulations show that the time of convergence and the mean-square error in the case of simulation are much smaller than those of the classical adaptive filter with Widrows LMS algorithm for both deterministic and random signal.