On delayed prediction of individual sequences

We investigate a prediction scenario in which the predictor is forced to make a decision a number of steps in advance, with incomplete information. For finite action and observation spaces, it is shown that the strategy that minimizes the worst-case regret with respect to the Bayes envelope is obtained through sub-sampling of the sequence of observations. The result extends to the case of logarithmic loss.