CLPVG: Circular limited penetrable visibility graph as a new network model for time series

TL;DR

This paper introduces CLPVG, a novel circular limited penetrable visibility graph model for time series analysis that captures key features more effectively and with better noise resistance than traditional methods, improving classification accuracy.

Contribution

The paper proposes a new nonlinear mapping method called CLPVG, enhancing feature extraction from time series and improving classification performance over existing visibility graph models.

Findings

CLPVG effectively captures important features of time series.

CLPVG demonstrates superior noise resistance compared to LPVG.

Structural features from CLPVG improve time-series classification accuracy.

Abstract

Visibility Graph (VG) transforms time series into graphs, facilitating signal processing by advanced graph data mining algorithms. In this paper, based on the classic Limited Penetrable Visibility Graph (LPVG) method, we propose a novel nonlinear mapping method named Circular Limited Penetrable Visibility Graph (CLPVG). The testing on degree distribution and clustering coefficient on the generated graphs of typical time series validates that our CLPVG is able to effectively capture the important features of time series and has better anti-noise ability than traditional LPVG. The experiments on real-world time-series datasets of radio signal and electroencephalogram (EEG) also suggest that the structural features provided by CLPVG, rather than LPVG, are more useful for time-series classification, leading to higher accuracy. And this classification performance can be further enhanced through structural feature expansion by adopting Subgraph Networks (SGN). All of these results validate the effectiveness of our CLPVG model.