Optimal Prediction Risks for Hidden Markov Models and Renewal Processes with Infinite Memory

The authors determine the optimal prediction risk in Kullback-Leibler divergence for hidden Markov models and renewal processes, which have infinite memory, up to universal constant factors. They propose a prediction algorithm based on universal compression that achieves the optimal risk.