Relation Between Autocorrelation Sequence and Average Shortest-Path Length in a Time Serie to Network Mapping
DOI:
https://doi.org/10.5890/DNC.2019.06.010Abstract
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 time series are mapped in the topological measures of associated networks. More specifically, we employ filtered uniform white noise and analyse how the autocorrelation sequence influences the average shortest-path length.References
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