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Polynomial prediction using incomplete data

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