English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Visibility Graph (VG) transforms time series into graphs, facilitating signalprocessing by advanced graph data mining algorithms. In this paper, based onthe classic Limited Penetrable Visibility Graph (LPVG) method, we propose anovel nonlinear mapping method named Circular Limited Penetrable VisibilityGraph (CLPVG). The testing on degree distribution and clustering coefficient onthe generated graphs of typical time series validates that our CLPVG is able toeffectively capture the important features of time series and has betteranti-noise ability than traditional LPVG. The experiments on real-worldtime-series datasets of radio signal and electroencephalogram (EEG) alsosuggest that the structural features provided by CLPVG, rather than LPVG, aremore useful for time-series classification, leading to higher accuracy. Andthis classification performance can be further enhanced through structuralfeature expansion by adopting Subgraph Networks (SGN). All of these resultsvalidate the effectiveness of our CLPVG model.

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