Best linear prediction for α-stable random processes

The best linear prediction for α-stable random processes based on some past values is presented. The prediction is the best with respect to a criterion known as stable covariation. The minimum stable covariations can be considered as the smallest error tail probabilities. The predictor obtained is equal to the best linear predictor based on minimization of second-moment error for Gaussian processes.