Similarities and differences between one-sided and two-sided linear prediction

Provides a comparison between one-sided linear prediction (OSP) and two-sided linear prediction (TSP) with respect to prediction error, relationships to AR modeling and to two-sided AR modeling, and the application to time series interpolation, linear-phase filter design, and spectral estimation. Although some of these results have appeared previously in scattered references, the authors present a unified framework for deriving all of these results, as well as new, additional results. New contributions of the paper include: (i) proof that TSP produces smaller, nonwhite residuals than OSP, extending previous results; (ii) specification of the frequency-domain error criterion minimized by TSP, and comparison with the analogous OSP criterion; (iii) demonstration that TSP and two-sided AR modeling are different problems, unlike OSP; (iv) interpretation of performance of TSP interference-rejection filters