title: Multiscale limited penetrable horizontal visibility graph for analyzing nonlinear time series.
abstract: Visibility graph has established itself as a powerful tool for analyzing time series. We in this paper develop a novel multiscale limited penetrable horizontal visibility graph (MLPHVG). We use nonlinear time series from two typical complex systems, i.e., EEG signals and two-phase flow signals, to demonstrate the effectiveness of our method. Combining MLPHVG and support vector machine, we detect epileptic seizures from the EEG signals recorded from healthy subjects and epilepsy patients and the classification accuracy is 100%. In addition, we derive MLPHVGs from oil-water two-phase flow signals and find that the average clustering coefficient at different scales allows faithfully identifying and characterizing three typical oil-water flow patterns. These findings render our MLPHVG method particularly useful for analyzing nonlinear time series from the perspective of multiscale network analysis.
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
authors.forename: Zhong-Ke
authors.initials: ZK
authors.lastname: Gao
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
authors.forename: Qing
authors.initials: Q
authors.lastname: Cai
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
authors.forename: Yu-Xuan
authors.initials: YX
authors.lastname: Yang
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
authors.forename: Wei-Dong
authors.initials: WD
authors.lastname: Dang
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
authors.forename: Shan-Shan
authors.initials: SS
authors.lastname: Zhang