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Symbol sequences are generated from observed time series data to construct probabilistic finite state automata (PFSA) models that capture the evolution of the dynamical system under consideration. One of the key challenges here is to estimate the relevant history or depth (i.e., the size of temporal memory) of the symbol sequences; in this context, spectral decomposition of the one-step transition...
Probabilistic finite state automata (PFSA) are constructed from symbol sequences for modeling the behavior of dynamical systems. This paper presents construction of finite history automata from symbol sequences; such automata, called D-Markov machines, are structurally simple and computationally efficient. The construction procedure is based on: (i) state splitting that generates symbol blocks of...
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