Time series characterization via horizontal visibility graph and Information Theory

2016 ◽  
Vol 464 ◽  
pp. 93-102 ◽  
Author(s):  
Bruna Amin Gonçalves ◽  
Laura Carpi ◽  
Osvaldo A. Rosso ◽  
Martín G. Ravetti
Keyword(s):  
Information Theory ◽  
Time Series ◽  
Visibility Graph ◽  
Horizontal Visibility ◽  
Horizontal Visibility Graph

scholarly journals Analysing the Dynamics of Interbeat Interval Time Series Using Grouped Horizontal Visibility Graph

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 9926-9934 ◽  
Author(s):  
Gulraiz Iqbal Choudhary ◽  
Wajid Aziz ◽  
Ishtiaq Rasool Khan ◽  
Susanto Rahardja ◽  
Pasi Franti
Keyword(s):  
Time Series ◽  
Visibility Graph ◽  
Interbeat Interval ◽  
Horizontal Visibility ◽  
Interval Time ◽  
Interval Time Series ◽  
Horizontal Visibility Graph

Inference On Streamflow Predictability Using Horizontal Visibility Graph Based Networks

2020 ◽  
Author(s):  
Ganesh Ghimire ◽  
Navid Jadidoleslam ◽  
Witold Krajewski ◽  
Anastasios Tsonis
Keyword(s):  
Time Series ◽  
Time Scale ◽  
Temporal Trends ◽  
Characteristic Exponent ◽  
Visibility Graph ◽  
Clustering Coefficient ◽  
Series Data ◽  
Dynamical Process ◽  
Horizontal Visibility ◽  
Horizontal Visibility Graph

<p>Streamflow is a dynamical process that integrates water movement in space and time within basin boundaries. The authors characterize the dynamics associated with streamflow time series data from about seventy-one U.S. Geological Survey (USGS) stream-gauge stations in the state of Iowa. They employ a novel approach called visibility graph (VG). It uses the concept of mapping time series into complex networks to investigate the time evolutionary behavior of dynamical system. The authors focus on a simple variant of VG algorithm called horizontal visibility graph (HVG). The tracking of dynamics and hence, the predictability of streamflow processes, are carried out by extracting two key pieces of information called characteristic exponent, λ of degree distribution and global clustering coefficient, GC pertaining to HVG derived network. The authors use these two measures to identify whether streamflow process has its origin in random or chaotic processes. They show that the characterization of streamflow dynamics is sensitive to data attributes. Through a systematic and comprehensive analysis, the authors illustrate that streamflow dynamics characterization is sensitive to the normalization, and the time-scale of streamflow time-series. At daily scale, streamflow at all stations used in the analysis, reveals randomness with strong spatial scale (basin size) dependence. This has implications for predictability of streamflow and floods. The authors demonstrate that dynamics transition through potentially chaotic to randomly correlated process as the averaging time-scale increases. Finally, the temporal trends of λ and GC are statistically significant at about 40% of the total number of stations analyzed. Attributing this trend to factors such as changing climate or land use requires further research.</p>

scholarly journals Multiscale limited penetrable horizontal visibility graph for analyzing nonlinear time series

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Zhong-Ke Gao ◽  
Qing Cai ◽  
Yu-Xuan Yang ◽  
Wei-Dong Dang ◽  
Shan-Shan Zhang
Keyword(s):  
Time Series ◽  
Nonlinear Time Series ◽  
Visibility Graph ◽  
Horizontal Visibility ◽  
Horizontal Visibility Graph

Jensen–Shannon Divergence Based on Horizontal Visibility Graph for Complex Time Series

2020 ◽  
pp. 2150013
Author(s):  
Yi Yin ◽  
Wenjing Wang ◽  
Qiang Li ◽  
Zunsong Ren ◽  
Pengjian Shang
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Visibility Graph ◽  
Horizontal Visibility ◽  
Stock Indices ◽  
Leibler Divergence ◽  
Time Irreversibility ◽  
Jensen Shannon Divergence ◽  
Time Series Irreversibility ◽  
Horizontal Visibility Graph

In this paper, we propose Jensen–Shannon divergence (JSD) based on horizontal visibility graph (HVG) to measure the time series irreversibility for both stationary and non-stationary series efficiently. Numerical simulations are first conducted to show the validity of the proposed method and then empirical applications to the financial time series and traffic time series are investigated. It can be found that JSD shows better robustness than Kullback–Leibler divergence (KLD) on quantifying time series irreversibility and correctly distinguishes the different type of simulated series. For the empirical analysis, JSD based on HVG is able to detect the significant time irreversibility of stock indices and reveal the relationship between different stock indices. JSD results show the time irreversibility of speed time series for different detectors and present better accuracy and robustness than KLD. The hierarchical clustering based on their behavior of time irreversibility obtained by JSD classifies the detectors into four groups.

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