"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."

Fioriti, V., Tofani, A., Di Pietro, A. (2012). Discriminating chaotic time series with visibility graph eigenvalues. In Complex Systems (pp. 193-200).

Discriminating chaotic time series with visibility graph eigenvalues

Di Pietro, Antonio
2012-01-01

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."
Fioriti, V., Tofani, A., Di Pietro, A. (2012). Discriminating chaotic time series with visibility graph eigenvalues. In Complex Systems (pp. 193-200).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/279136
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