Predicting the Future of Discrete Sequences from Fractal Representations of the Past

@article{Tio2001PredictingTF,
  title={Predicting the Future of Discrete Sequences from Fractal Representations of the Past},
  author={Peter Ti{\~n}o and Georg Dorffner},
  journal={Machine Learning},
  year={2001},
  volume={45},
  pages={187-217}
}
We propose a novel approach for building finite memory predictive models similar in spirit to variable memory length Markov models (VLMMs). The models are constructed by first transforming the n-block structure of the training sequence into a geometric structure of points in a unit hypercube, such that the longer is the common suffix shared by any two n-blocks, the closer lie their point representations. Such a transformation embodies a Markov assumption—n-blocks with long common suffixes are… CONTINUE READING