Multifractal analysis of visibility graph-based Ito-related connectivity time series

2016 ◽  
Vol 26 (2) ◽  
pp. 023118 ◽  
Author(s):  
Zbigniew Czechowski ◽  
Michele Lovallo ◽  
Luciano Telesca
Keyword(s):  
Time Series ◽  
Multifractal Analysis ◽  
Visibility Graph

scholarly journals Multifractal Analysis of Hydrologic Data Using Wavelet Methods and Fluctuation Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Tongzhou Zhao ◽  
Liang Wu ◽  
Dehua Li ◽  
Yiming Ding
Keyword(s):  
Time Series ◽  
Water Level ◽  
Multifractal Analysis ◽  
Simulation Models ◽  
Northern China ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Wavelet Leaders ◽  
Detailed Characteristic ◽  
Wavelet Methods

We study the multifractal properties of water level with a high-frequency and massive time series using wavelet methods (estimation of Hurst exponents, multiscale diagram, and wavelet leaders for multifractal analysis (WLMF)) and multifractal detrended fluctuation analysis (MF-DFA). The dataset contains more than two million records from 10 observation sites at a northern China river. The multiscale behaviour is observed in this time series, which indicates the multifractality. This multifractality is detected via multiscale diagram. Then we focus on the multifractal analysis using MF-DFA and WLMF. The two methods give the same conclusion that at most sites the records satisfy the generalized binomial multifractal model, which is robust for different times (morning, afternoon, and evening). The variation in the detailed characteristic parameters of the multifractal model indicates that both human activities and tributaries influence the multifractality. Our work is useful for building simulation models of the water level of local rivers with many observation sites.

scholarly journals Extracting Correlations in Earthquake Time Series Using Visibility Graph Analysis

2021 ◽  
Vol 9 ◽  
Author(s):  
Sumanta Kundu ◽  
Anca Opris ◽  
Yohei Yukutake ◽  
Takahiro Hatano
Keyword(s):  
Time Series ◽  
Complex Network ◽  
Recent Observation ◽  
Visibility Graph ◽  
Graph Analysis ◽  
Common Belief ◽  
Complex Network Theory ◽  
Event Time ◽  
Visibility Graph Analysis ◽  
Present Understanding

Recent observation studies have revealed that earthquakes are classified into several different categories. Each category might be characterized by the unique statistical feature in the time series, but the present understanding is still limited due to their non-linear and non-stationary nature. Here we utilize complex network theory to shed new light on the statistical properties of earthquake time series. We investigate two kinds of time series, which are magnitude and inter-event time (IET), for three different categories of earthquakes: regular earthquakes, earthquake swarms, and tectonic tremors. Following the criterion of visibility graph, earthquake time series are mapped into a complex network by considering each seismic event as a node and determining the links. As opposed to the current common belief, it is found that the magnitude time series are not statistically equivalent to random time series. The IET series exhibit correlations similar to fractional Brownian motion for all the categories of earthquakes. Furthermore, we show that the time series of three different categories of earthquakes can be distinguished by the topology of the associated visibility graph. Analysis on the assortativity coefficient also reveals that the swarms are more intermittent than the tremors.

scholarly journals Visibility Graph Power Geometric Aggregation Operator and Its Application in Water, Energy and Food Efficiency Evaluation

2020 ◽  
Vol 17 (11) ◽  
pp. 3891
Author(s):  
Lihua Liu ◽  
Jing Huang ◽  
Huimin Wang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Coupling Efficiency ◽  
Aggregation Operator ◽  
Visibility Graph ◽  
Series Data ◽  
Efficiency Evaluation ◽  
Geometric Operator ◽  
The Relationship

In the real decision-making process, there are so many time series values that need to be aggregated. In this paper, a visibility graph power geometric (VGPG) aggregation operator is developed, which is based on the complex network and power geometric operator. Time series data are converted into a visibility graph. A visibility matrix is developed to denote the links among different time series values. A new support function based on the distance of two values are proposed to measure the support degree of each other when the two time series values have visibility. The VGPG operator considers not only the relationship but also the similarity degree between two values. Meanwhile, some properties of the VGPG operator are also investigated. Finally, a case study for water, energy, and food coupling efficiency evaluation in China is illustrated to show the effectiveness of the proposed operator. Comparative analysis with the existing research is also offered to show the advantages of the proposed method.

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
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