Complex Systems

Discriminating Chaotic Time Series with Visibility Graph Eigenvalues Download PDF

Vincenzo Fioriti
Alberto Tofani
Antonio Di Pietro
Italian National Agency for New Technologies, Energy and Sustainable
Economic Development (ENEA), CR Casaccia Labs
S. Maria in Galeria
301,00130 Rome, Italy

Abstract

Time series can be transformed into graphs called horizontal visibility graphs (HVGs) in order to gain useful insights. Here, the maximum eigenvalue of the adjacency matrix associated to the HVG derived from several time series is calculated. The maximum eigenvalue methodology is able to discriminate between chaos and randomness and is suitable for short time series, hence for experimental results. An application to the United States gross domestic product data is given.