Recurrence plot based time series analysis is widely used to study changes and transitions in the dynamics of a system or temporal deviations from its overall dynamical regime. However, most studies do not discuss the significance of the detected variations in the recurrence quantification measures. In this letter we propose a novel method to add a confidence measure to the recurrence quantification analysis. We show how this approach can be used to study significant changes in dynamical systems due to a change in control parameters, chaos-order as well as chaos-chaos transitions. Finally we study and discuss climate transitions by analysing a marine proxy record for past sea surface temperature. This paper is dedicated to the 25th anniversary of the introduction of recurrence plots.
Leveraging temporal features of the divergence quantifier of recurrence plot to detect chaos in conservative systems
Université de Toulon, Aix Marseille Univ, CNRS, CPT（土伦大学，艾克斯-马赛大学，法国国家科学研究中心，理论物理中心）；Institute of Physics and Astronomy, Eötvös University（罗兰大学物理与天文学研究所）
Deep learning for classifying dynamical states from time series via recurrence plots
Indian Institute of Science Education and Research (IISER), Pune（印度科学教育研究所（IISER），浦那）；Indian Institute of Science Education and Research (IISER), Thiruvananthapuram（印度科学教育研究所（IISER），特里凡得琅）
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association（波茨坦气候影响研究所（PIK），莱布尼茨协会成员）；University of Potsdam, Institute of Geoscience（波茨坦大学，地球科学研究所）；University of Potsdam, Institute of Physics and Astronomy（波茨坦大学，物理与天文学研究所）
