  In this paper, we derive generic bounds on the maximum deviations in
prediction errors for sequential prediction via an information-theoretic
approach. The fundamental bounds are shown to depend only on the conditional
entropy of the data point to be predicted given the previous data points. In
the asymptotic case, the bounds are achieved if and only if the prediction
error is white and uniformly distributed.
