An adaptive d-step ahead predictor based on least squares

This paper examines the asymptotic properties of a least squares algorithm for adaptively calculating a
d
-step ahead prediction of a time series. It is shown that, with probability one, the sample mean-square difference between time recursive prediction and the optimal linear prediction converges to zero. Relatively weak assumptions are required regarding the underlying model of the time series.