Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.25088/ComplexSystems.21.3.193 | COMPLEX SYSTEMS |
Field | DocType | Volume |
Adjacency matrix,Discrete mathematics,Visibility graph,Artificial intelligence,Chaotic,Eigenvalues and eigenvectors,Mathematics,Machine learning | Journal | 21 |
Issue | ISSN | Citations |
3 | 0891-2513 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vincenzo Fioriti | 1 | 41 | 7.09 |
| Alberto Tofani | 2 | 27 | 7.00 |
| Antonio Di Pietro | 3 | 17 | 5.64 |