From the time series to the complex networks: the parametric (dynamical) natural visibility graph

TL;DR

This paper introduces the parametric natural visibility graph (PNVG), a modified algorithm that maps time series to complex networks using a view angle parameter, enhancing the analysis of different types of time series.

Contribution

The paper proposes the PNVG algorithm, adding a view angle parameter to the natural visibility graph, enabling more detailed characterization of time series structures.

Findings

PNVG properties vary with view angle for different time series.

PNVG can distinguish between healthy and ill cardiac rhythms.

View angle provides new insights into time series structure.

Abstract

We present the modification of natural visibility graph (NVG) algorithm used for the mapping of the time series to the complex networks (graphs). We propose the parametric natural visibility graph (PNVG) algorithm. The PNVG consists of NVG links, which satisfy an additional constraint determined by a newly introduced continuous parameter - the view angle. The alteration ofview angle modifiesthe PNVG and its properties such as the average node degree, average link length of the graph as well as cluster quantity of built graph etc. Wecalculated and analyzed different PNVG properties depending on the view angle for different types of the time series such as the random (uncorrelated, correlated and fractal) and cardiac rhythm time series for healthy and ill patients. Investigation of different PNVG properties shows that the view angle gives a new approach to characterize the structure of the time series that are invisible in the conventional version of the algorithm. It is also shown that the PNVG approach allows to distinguish, identify and describe in detail various time series.