On delayed prediction of individual sequences

Prediction of individual sequences is investigated for cases in which the decision maker observes a delayed version of the sequence, or is forced to issue his/her predictions a number of steps in advance, with incomplete information. For finite action and observation spaces, it is shown that the prediction strategy that minimizes the worst case regret with respect to the Bayes envelope is obtained through subsampling of the sequence of observations. The result extends to the case of logarithmic loss. For finite-state (FS) reference prediction strategies, the delayed FS predictability (DFSP) is defined and related to its nondelayed counterpart. As in the nondelayed case, an efficient on-line decision algorithm, based on the incremental parsing rule, is shown to perform in the long run essentially as well as the best FS strategy determined in hindsight, with full knowledge of the given sequence of observations. An application to adaptive prefetching in computer memory architectures is discussed