We present a new method for nonlinear prediction of discrete random sequences under minimal structural assumptions. We give a mathematical construction for optimal predictors of such processes, in the form of hidden Markov models. We then describe an algorithm, CSSR (Causal-State Splitting Reconstruction), which approximates the ideal predictor from data. We discuss the reliability of CSSR, its data requirements, and its performance in simulations. Finally, we compare our approach to existing methods using variablelength Markov models and cross-validated hidden Markov models, and show theoretically and experimentally that our method delivers results superior to the former and at least comparable to the latter.
One modeling method closely related to prediction profiles is Causal State Splitting Reconstruction (CSSR) REF .
Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences
pp. 504--511 in Max Chickering and Joseph Halpern (eds.), _Uncertainty in Artificial Intelligence: Proceedings of the Twentieth Conference_ (2004)
Computer Science Mathematics Physics