Using the Memories of Multiscale Machines to Characterize Complex Systems

Details Using the Memories of Multiscale Machines to Characterize Complex Systems Using the Memories of Multiscale Machines to Characterize Complex Systems

2008-12-30

arxiv.org/abs/0812.5079v1

Phys. Rev. Lett. 100, 208702 (2008)

Physics

Data Analysis, Statistics and Probability

4 pages, 2 figures

Scientific paper

10.1103/PhysRevLett.100.208702

A scheme is presented to extract detailed dynamical signatures from successive measurements of complex systems. Relative entropy based time series tools are used to quantify the gain in predictive power of increasing past knowledge. By lossy compression, data is represented by increasingly coarsened symbolic strings. Each compression resolution is modeled by a machine: a finite memory transition matrix. Applying the relative entropy tools to each machine's memory exposes correlations within many time scales. Examples are given for cardiac arrhythmias and different heart conditions are distinguished.

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