| Title | Discriminating chaotic time series with visibility graph eigenvalues |
|---|---|
| Publication Type | Articolo su Rivista peer-reviewed |
| Year of Publication | 2012 |
| Authors | Fioriti, Vincenzo, Tofani A., and Di Pietro A. |
| Journal | Complex Systems |
| Volume | 21 |
| Pagination | 193-200 |
| ISSN | 08912513 |
| Keywords | Adjacency matrices, Chaotic time series, Eigen-value, Eigenvalues and eigenfunctions, Gross domestic products, Horizontal visibility graphs, Short time series, Time series, Visibility graphs |
| 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. © 2012 Complex Systems Publications, Inc. |
| Notes | cited By 2 |
| URL | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84875357101&partnerID=40&md5=be67fdeb5bb1718df557c876cf753068 |
| Citation Key | Fioriti2012193 |
