Recurrence Plot

Recurrence plots (RPs) are visual representations of the recurrence of states in a dynamical system's time series, revealing patterns and structures indicative of the system's underlying dynamics. Current research focuses on leveraging RPs, often in conjunction with recurrence quantification analysis (RQA) and machine learning algorithms like RNNs, LSTMs, and support vector machines, to classify different dynamical states (e.g., chaotic vs. periodic), detect anomalies (e.g., in ECG signals or fMRI data), and even infer shared causal drivers across multiple time series. This approach finds applications in diverse fields, from financial market analysis and medical diagnosis to understanding complex biological systems and improving multi-agent coordination, demonstrating the broad utility of RPs in analyzing complex temporal data.