Time series characterization via horizontal visibility graph and Information Theory

• This work deals with the characterization of dynamical systems using Horizontal Visibility Graphs (HVG) and Information Theory quantifiers.
• We propose the use of the weight distribution, which is based on the difference of the time series values of connected points.
• We study fractional Brownian motion time series and a paleoclimatic proxy record of ENSO taken from Pallcacocha Lake.
• The weight distribution allows a better characterization of the studied systems, using considerable shorter time series.

Complex networks theory have gained wider applicability since methods for transformation of time series to networks were proposed and successfully tested. In the last few years, horizontal visibility graph has become a popular method due to its simplicity and good results when applied to natural and artificially generated data. In this work, we explore different ways of extracting information from the network constructed from the horizontal visibility graph and evaluated by Information Theory quantifiers. Most works use the degree distribution of the network, however, we found alternative probability distributions, more efficient than the degree distribution in characterizing dynamical systems. In particular, we find that, when using distributions based on distances and amplitude values, significant shorter time series are required. We analyze fractional Brownian motion time series, and a paleoclimatic proxy record of ENSO from the Pallcacocha Lake to study dynamical changes during the Holocene.