We derive an FIR polynomial predictor for data in which some samples a
re missing. The method is compared with a computationally lighter algo
rithm that is based on decision-driven recursion. Both schemes are fou
nd to perform almost identically well on predicting a sinusoidal signa
l corrupted by both impulsive and Gaussian noise.