Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences
<|reference_start|>Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences: 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 variable-length 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.<|reference_end|>
@article{shalizi2004blind,
    title={Blind Construction of Optimal Nonlinear Recursive Predictors for
  Discrete Sequences},
    author={Cosma Rohilla Shalizi and Kristina Lisa Shalizi},
    journal={pp. 504--511 in Max Chickering and Joseph Halpern (eds.),
  _Uncertainty in Artificial Intelligence: Proceedings of the Twentieth
  Conference_ (2004)},
    year={2004},
    archivePrefix={arXiv},
    eprint={cs/0406011},
    primaryClass={cs.LG math.ST nlin.CD physics.data-an stat.TH}
}