Universal context tree least squares prediction

We investigate the problem of sequential prediction of individual sequences using a competitive algorithm approach. We have previously developed prediction algorithms that are universal with respect to the class of all linear predictors, such that the prediction algorithm competes against a continuous class of prediction algorithms, under the square error loss. In this paper, we introduce the use of a "context tree," to compete against a doubly exponential number of piecewise linear models. We use the context tree to achieve the performance of the best piecewise linear model that can choose its partition of the real line and real-valued prediction parameters, based on observing the entire sequence in advance, for the square error loss, uniformly, for any individual sequence. This performance is achieved with a prediction algorithm whose complexity is only linear in the depth of the context tree.