Building Predictive Models from Fractal Representations of Symbolic Sequences
Peter Tiňo, Georg Dorffner · 1999
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 then-block structure of the training sequence into a spatial structure of points in a unit hypercube, such that the longer is the common suffix shared by any twon-blocks, the closer lie their point representations. Such a transformation embodies a Markov assumption –n-blocks with long common suffixes are likely to produce similar continuations. Finding a set of prediction contexts is formulated as a resource allocation problem solved by vector quantizing the spatialn-block representation. We compare our model with both the classical and variable memory length Markov models on three data sets with different memory and stochastic components. Our models have a superior performance, yet, their construction is fully automatic, which is shown to be problematic in the case of VLMMs. 1