Laib Mohamed, Telesca Luciano, Kanevski Mikhail
IDYST, Faculty of Geosciences and Environment, University of Lausanne, 1015 Lausanne, Switzerland.
CNR, Istituto di Metodologie per l'Analisi Ambientale, 85050 Tito (PZ), Italy.
Chaos. 2018 Mar;28(3):033108. doi: 10.1063/1.5022737.
This paper studies the daily connectivity time series of a wind speed-monitoring network using multifractal detrended fluctuation analysis. It investigates the long-range fluctuation and multifractality in the residuals of the connectivity time series. Our findings reveal that the daily connectivity of the correlation-based network is persistent for any correlation threshold. Further, the multifractality degree is higher for larger absolute values of the correlation threshold.
本文运用多重分形去趋势波动分析方法研究了风速监测网络的每日连通性时间序列。它考察了连通性时间序列残差中的长程波动和多重分形特性。我们的研究结果表明，基于相关性的网络的每日连通性对于任何相关性阈值都是持续存在的。此外，相关性阈值的绝对值越大，多重分形程度越高。
