Our analysis of the problems with Spitz's method suggests that the prediction filter at a given frequency should be computed from the data component of that frequency. However, prediction filters computed from high-frequency components of data may suffer from contamination by spatially aliased energy. This section first shows that spatially aliased energy creates ambiguities regarding the zeros of the z-transform of the prediction filters and describes how to use a neural net to remove these ambiguities.