# Near-ideal predictors and causal filters for discrete time signals

## TL;DR

This paper introduces near-ideal linear predictors and causal filters for discrete time signals with spectrum degeneracy, using polynomial approximations of ideal transfer functions to achieve effective causality.

## Contribution

It proposes a novel method for designing causal filters and predictors based on polynomial approximation of non-causal transfer functions for signals with spectrum degeneracy.

## Key findings

- Effective causal filters for spectrum-degenerate signals
- Polynomial approximation of transfer functions improves predictor performance
- Applicable to a range of discrete time signal processing tasks

## Abstract

The paper presents linear predictors and causal filters for discrete time signals featuring some different kinds of spectrum degeneracy. These predictors and filters are based on approximation of ideal non-causal transfer functions by causal transfer functions represented by polynomials of Z-transform of the unit step signal.