Visibility Graph Based Time Series Analysis

Stephen, Mutua; Gu, Changgui; Yang, Huijie

doi:10.1371/journal.pone.0143015

Cited by 95 publications

(45 citation statements)

References 49 publications

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“…In this case, the horizontal visibility satisfies an ordering criterion. The visibility algorithm is simple to implement and has been applied in many different fields (e.g., [39,40,41,42,43]), including fluid flows [31,29,30,32,44,45]. However, the visibility approach has some drawbacks, related to the fact that it is invariant under affine transformations [15] (i.e., rescaling and translation of both horizontal and vertical axes), so this could lead to a lost of information in mapping the time-series.…”

Section: Mapping Time-series Into Network: the Visibility Algorithmmentioning

confidence: 99%

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Visibility graph analysis of wall turbulence time-series

2018

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The spatio-temporal features of the velocity field of a fully-developed turbulent channel flow are investigated through the natural visibility graph (NVG) method, which is able to fully map the intrinsic structure of the time-series into complex networks. Time-series of the three velocity components, (u,v,w), are analyzed at fixed grid-points of the whole three-dimensional domain. Each time-series was mapped into a network by means of the NVG algorithm, so that each network corresponds to a grid-point of the simulation. The degree centrality, the transitivity and the here proposed mean link-length were evaluated as indicators of the global visibility, inter-visibility, and mean temporal distance among nodes, respectively. The metrics were averaged along the directions of homogeneity (x, z) of the flow, so they only depend on the wall-normal coordinate, y^+. The visibility-based networks, inheriting the flow field features, unveil key temporal properties of the turbulent time-series and their changes moving along y^+. Although intrinsically simple to be implemented, the visibility graph-based approach offers a promising and effective support to the classical methods for accurate time-series analyses of inhomogeneous turbulent flows

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Section: Mapping Time-series Into Network: the Visibility Algorithmmentioning

confidence: 99%

“…as emerges from successive works [29,39,30,32,31,41]. Specifically, periodic time-series are converted into regular networks, i.e.…”

Section: Building the Networkmentioning

confidence: 99%

Visibility graph analysis of wall turbulence time-series

2018

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“…cutoffs/thresholds) (Liu et al, 2015). VGs have been widely applied in many fields (Supriya et al, 2016;Stephen et al, 2015;Bezsudnov and Snarskii, 2014). However, as we discussed above, the VG has two disadvantages: first, it does not consider the effect of uneven sampling; second, it cannot capture the time series changes below a zero baseline.…”

Section: Introductionmentioning

confidence: 99%

Visibility Graph Based Community Detection for Biological Time Series

Min

Domanskyi

Piermarocchi

et al. 2020

Preprint

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MotivationTemporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems: (i) How to create the appropriate network to reflect the characteristics of biological time series. (ii) How to detect characteristic temporal patterns or events as network communities. General methods to detect communities have used metrics to compare the connectivity within a community to the connectivity one would expect in a random model, or assumed a known number of communities, or are based on the betweenness centrality of edges or nodes. However, such methods were not specifically designed for network representations of time series. We introduce a visibility-graph-based method to build networks from different kinds of biological time series and detect temporal communities within these networks.ResultsTo characterize the uneven sampling of typical experimentally obtained biological time series, and simultaneously capture events associated to peaks and troughs, we introduce the Weighted Dual-Perspective Visibility Graph (WDPVG) for time series. To detect communities, we first find the shortest path of the network between start and end nodes to identify nodes which have high intensities. This identifies the main stem of our community detection algorithm. Then, we aggregate nodes outside the shortest path to the nodes found on the main stem based on the closest path length. Through simulation, we demonstrate the validity of our method in detecting community structures on various networks derived from simulated time series. We also confirm its effectiveness in revealing temporal communities in experimental biological time series. Our results suggest our method of visibility graph based community detection can be effective in detecting temporal biological patterns.AvailabilityThe methods of building WDPVG and visibility graph based community detection are available as a module of the open source Python package PyIOmica (https://doi.org/10.5281/zenodo.3691912) with documentation at https://pyiomica.readthedocs.io/en/latest/. The dataset and codes we used in this manuscript are publicly available at https://doi.org/10.5281/zenodo.3693984.Contactgmias@msu.edu

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“…The changing context of human and climate-induced changes in hydrologic cycle manifest into a new realm of streamflow predictability (Kumar, 2011) in addition to its traditional understanding in the context of water management and forecasting. In this study, the authors treat streamflow time-series as an output of the non-linear dynamical system and map it into complex networks using visibility-graph-based algorithm (e.g., Braga et al, 2016;Lacasa et al, 2012;Lacasa and Toral, 2010;Lacasa et al, 2008;Stephen et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Inference On Streamflow Predictability Using Horizontal Visibility Graph Based Networks

Ghimire¹,

Jadidoleslam²,

Krajewski³

et al. 2020

Preprint

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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.

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Visibility Graph Based Time Series Analysis

Cited by 95 publications

References 49 publications

Visibility graph analysis of wall turbulence time-series

Visibility graph analysis of wall turbulence time-series

Visibility Graph Based Community Detection for Biological Time Series

Inference On Streamflow Predictability Using Horizontal Visibility Graph Based Networks

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