Phase-Transition in Binary Sequences with Long-Range Correlations

Abstract

Motivated by novel results in the theory of correlated sequences, we analyze the dynamics of random walks with long-term memory (binary chains with long-range correlations). In our model, the probability for a unit bit in a binary string depends on the fraction of unities preceding it. We show that the system undergoes a dynamical phase-transition from normal diffusion, in which the variance D_L scales as the string's length L, into a super-diffusion phase (D_L ~ L^{1+|alpha|}), when the correlation strength exceeds a critical value. We demonstrate the generality of our results with respect to alternative models, and discuss their applicability to various data, such as coarse-grained DNA sequences, written texts, and financial data.

4 pages, 4 figures

References in corpus (2)

• Continuously-variable survival exponent for random walks with movable partial reflectors
• Time-Dependent Random Walks and the Theory of Complex Adaptive Systems

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