# Machine learning approach to detect dynamical states from recurrence measures

arXiv: https://arxiv.org/abs/2401.10298 · HTML: https://mlsanity.com/paper/2401.10298/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 70 references that are in the map.
- First posted on arXiv 18 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [Model-free tracking control of complex dynamical trajectories with machine learning](https://mlsanity.com/paper/2309.11470/) (2023)
- [Machine Learning Partners in Criminal Networks](https://mlsanity.com/paper/2209.03171/) (2022)
- [Recurrence Quantification Analysis of Dynamic Brain Networks](https://mlsanity.com/paper/2001.03761/) (2020)
- [Constructing Multiclass Classifiers using Binary Classifiers Under Log-Loss](https://mlsanity.com/paper/2102.08184/) (2021)

## Abstract (arXiv)

We integrate machine learning approaches with nonlinear time series analysis, specifically utilizing recurrence measures to classify various dynamical states emerging from time series. We implement three machine learning algorithms Logistic Regression, Random Forest, and Support Vector Machine for this study. The input features are derived from the recurrence quantification of nonlinear time series and characteristic measures of the corresponding recurrence networks. For training and testing we generate synthetic data from standard nonlinear dynamical systems and evaluate the efficiency and performance of the machine learning algorithms in classifying time series into periodic, chaotic, hyper-chaotic, or noisy categories. Additionally, we explore the significance of input features in the classification scheme and find that the features quantifying the density of recurrence points are the most relevant. Furthermore, we illustrate how the trained algorithms can successfully predict the dynamical states of two variable stars, SX Her and AC Her from the data of their light curves.