Relation Between Autocorrelation Sequence and Average Shortest-Path Length in a Time Serie to Network Mapping
Amanda Leite Camargo1; Marcio Eisencraft2
1 Universidade Federal do ABC; 2 USP
Resumo
An invertible mapping between time series and networks was recently proposed. It can be used as a tool to figure out properties of the mapped time series. In the present work we use controlled artificial signals to numerically investigate how correlation properties of the time series are mapped in the topological measures of the associated network. More specifically, we employ filtered uniform white noise and analyze how the autocorrelation sequence influences the average shortest-path length.
Palavras-chave: Time Series Analysis; Nonlinear Dynamics and Complex Systems; Discrete Dynamical Systems; Noise; Mapping