Machine learning approach to detect dynamical states from recurrence measures

arXiv:2401.10298v2 Announce Type: replace-cross
Abstract: 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 …

abstract algorithms analysis arxiv cs.lg features logistic regression machine machine learning machine learning algorithms physics.data-an random regression series study support time series type vector