Classifying Complex Dynamical and Stochastic Systems via Physics-Based Recurrence Features

TL;DR

This paper introduces a novel approach using recurrence microstate features to enhance machine learning classification of dynamical systems and noise, achieving higher accuracy with reduced computational resources.

Contribution

It presents a new method combining recurrence microstate features with ML algorithms, improving classification accuracy and efficiency for complex dynamical and stochastic systems.

Findings

Recurrence microstate features effectively capture system dynamics.

The method reduces computational time and memory usage.

Enhanced classification accuracy over traditional raw data analysis.

Abstract

In this study, we employ the recently developed recurrence microstate probabilities as features to improve accuracy of several well-established machine learning (ML) algorithms. These algorithms are applied to classify discrete and continuous dynamical systems, as well as colored noise. We demonstrate that the dynamical characteristics quantified by this method are effectively captured in the recurrence microstate space, a space defined solely by the recurrence properties of the signal. This space change reduces dimensions, which also reduces the necessary time to perform calculations and obtain relevant information about the underlying system. Here, we also demonstrate that a few optimal machine learning (ML) algorithms are particularly effective for classification when combined with recurrence microstates. Furthermore, these new machine learning vectors significantly reduce memory usage and computational complexity, outperforming the direct analysis of raw data.