scholarly journals Multiscale Horizontal Visibility Graph Analysis of Higher-Order Moments for Estimating Statistical Dependency

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 1008 ◽  
Author(s):  
Keqiang Dong ◽  
Haowei Che ◽  
Zhi Zou
Keyword(s):  
Time Series ◽  
Information Exchange ◽  
Engine Performance ◽  
Visibility Graph ◽  
Stationary Time Series ◽  
Third Order ◽  
Horizontal Visibility ◽  
Statistical Dependency ◽  
Visibility Graph Analysis ◽  
Horizontal Visibility Graph

The horizontal visibility graph is not only a powerful tool for the analysis of complex systems, but also a promising way to analyze time series. In this paper, we present an approach to measure the nonlinear interactions between a non-stationary time series based on the horizontal visibility graph. We describe how a horizontal visibility graph may be calculated based on second-order and third-order statistical moments. We compare the new methods with the first-order measure, and then give examples including stock markets and aero-engine performance parameters. These analyses suggest that measures derived from the horizontal visibility graph may be of particular relevance to the growing interest in quantifying the information exchange between time series.

EXTREME EVENTS ANALYSIS OF NON-STATIONARY TIME SERIES BY USING HORIZONTAL VISIBILITY GRAPH

Fractals ◽  
2020 ◽  
Vol 28 (05) ◽  
pp. 2050089
Author(s):  
XIAOJUN ZHAO ◽  
JIE SUN ◽  
NA ZHANG ◽  
PENGJIAN SHANG
Keyword(s):  
Time Series ◽  
Random Walk ◽  
Extreme Events ◽  
Trading Volume ◽  
Temporal Structure ◽  
Visibility Graph ◽  
Stationary Time Series ◽  
Horizontal Visibility ◽  
Stationary Time ◽  
Horizontal Visibility Graph

In this paper, we analyze the extreme events of non-stationary time series in the framework of horizontal visibility graph (HVG). We give a new definition of extreme events, which incorporates the temporal structure of the series and the degree of the nodes in the HVG. An advantage of the new concept is that it does not require ad hoc treatment even when the non-stationarity arises in time series. We also use the information-theoretic methods to analyze the degree of nodes in the HVG. In the numerical analysis, we study the statistical characterizations of the extreme events of synthetic time series, including the random noises, periodic time series, random walk processes, and the long-range auto-correlated time series. Then, we study 9 time series in stock markets to identify the extreme events evolving in these non-stationary systems. Interestingly, we find that the daily closing price series perform rather close to the random walk processes, while the daily trading volume series behave quite similar to the random noises.

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
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