CLPVG: Circular limited penetrable visibility graph as a new network model for time series
This work addresses the need for more robust graph-based models in time-series analysis, particularly for applications in signal processing and classification, though it appears incremental as it builds on existing limited penetrable visibility graph methods.
The authors tackled the problem of transforming time series into graphs for improved signal processing by proposing a new method called Circular Limited Penetrable Visibility Graph (CLPVG), which showed better anti-noise ability and higher classification accuracy on real-world datasets like radio signals and EEG compared to traditional methods.
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.