Using the Memories of Multiscale Machines to Characterize Complex Systems

TL;DR

This paper introduces a method using multiscale memory-based models and relative entropy to analyze complex systems, revealing multi-scale correlations and aiding in distinguishing different cardiac conditions.

Contribution

It presents a novel approach combining lossy compression and finite memory models to extract multiscale dynamical signatures from complex systems.

Findings

Successfully distinguished different heart conditions using the method.

Revealed correlations across multiple time scales in complex systems.

Enhanced understanding of system dynamics through multiscale analysis.

Abstract

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.