Time series analysis in earthquake complex networks

2018 ◽  
Vol 28 (8) ◽  
pp. 083128 ◽  
Author(s):  
Denisse Pastén ◽  
Zbigniew Czechowski ◽  
Benjamín Toledo
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Complex Networks ◽  
Series Analysis

Are all the word ranking methods the same?

2021 ◽  
pp. 2150144
Author(s):  
Elham Najafi ◽  
Alireza Valizadeh ◽  
Amir H. Darooneh
Keyword(s):  
Complex System ◽  
Time Series ◽  
Spatial Distribution ◽  
Time Series Analysis ◽  
Complex Networks ◽  
Rank Correlation ◽  
Ranking Methods ◽  
Series Analysis ◽  
Maximum Value

Text as a complex system is commonly studied by various methods, like complex networks or time series analysis, in order to discover its properties. One of the most important properties of each text is its keywords, which are extracted by word ranking methods. There are various methods to rank words of a text. Each method differently ranks words according to their frequency, spatial distribution or other word properties. Here, we aimed to explore how similar various word ranking methods are. For this purpose, we studied the rank correlation of some important word ranking methods for number of sample texts with different subjects and text sizes. We found that by increasing text size the correlation between ranking methods grows. It means that as the text size increases, the associated word ranks calculated by different ranking methods converge. Also, we found out that the rank correlations of word ranking methods approach their maximum value in the case of large enough texts.

scholarly journals Nonlinear time series analysis by means of complex networks

2019 ◽  
Vol 50 (1) ◽  
pp. 010509
Author(s):  
null DONNER Reik V. ◽  
MARWAN Norbert ◽  
ZOU Yong ◽  
KURTHS Jürgen ◽  
null DONGES Jonathan F.
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Complex Networks ◽  
Nonlinear Time Series ◽  
Nonlinear Time Series Analysis ◽  
Series Analysis

NONLINEAR TIME SERIES INTO COMPLEX NETWORKS SCHEME

2010 ◽  
Vol 20 (02) ◽  
pp. 413-417 ◽  
Author(s):  
FRANCISCO O. REDELICO ◽  
ARACELI N. PROTO
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Complex Networks ◽  
Simulated Data ◽  
Nonlinear Time Series ◽  
New Method ◽  
Series Analysis ◽  
Extract Information

Time series analysis is a fundamental tool for a wide variety of fields. Different methods have been proposed to extract information about the underlying dynamics (basically nonlinear) contained inside the time series [Albert & Barabási, 2002]. In this contribution, a new method for mapping nonlinear time series into complex networks is studied using simulated data.

scholarly journals RECURRENCE-BASED TIME SERIES ANALYSIS BY MEANS OF COMPLEX NETWORK METHODS

2011 ◽  
Vol 21 (04) ◽  
pp. 1019-1046 ◽  
Author(s):  
REIK V. DONNER ◽  
MICHAEL SMALL ◽  
JONATHAN F. DONGES ◽  
NORBERT MARWAN ◽  
YONG ZOU ◽  
...  
Keyword(s):  
Time Series ◽  
Dynamical Systems ◽  
Time Series Analysis ◽  
Complex Networks ◽  
Complex Network ◽  
Structural Features ◽  
Recurrence Plots ◽  
Series Analysis ◽  
Network Methods ◽  
The Individual

Complex networks are an important paradigm of modern complex systems sciences which allows quantitatively assessing the structural properties of systems composed of different interacting entities. During the last years, intensive efforts have been spent on applying network-based concepts also for the analysis of dynamically relevant higher-order statistical properties of time series. Notably, many corresponding approaches are closely related to the concept of recurrence in phase space. In this paper, we review recent methodological advances in time series analysis based on complex networks, with a special emphasis on methods founded on recurrence plots. The potentials and limitations of the individual methods are discussed and illustrated for paradigmatic examples of dynamical systems as well as for real-world time series. Complex network measures are shown to provide information about structural features of dynamical systems that are complementary to those characterized by other methods of time series analysis and, hence, substantially enrich the knowledge gathered from other existing (linear as well as nonlinear) approaches.

Dynamical Peculiarities of Concatenational Changes in Functional Cardiovascular Parameters

2018 ◽  
Vol 3 (82) ◽  
Author(s):  
Eurelija Venskaitytė ◽  
Jonas Poderys ◽  
Tadas Česnaitis
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Functional State ◽  
The Body ◽  
Functional Changes ◽  
High Intensity Training ◽  
Series Analysis ◽  
Greco Roman ◽  
St Segment ◽  
Standard Lead

Research  background  and  hypothesis.  Traditional  time  series  analysis  techniques,  which  are  also  used  for the analysis of cardiovascular signals, do not reveal the relationship between the  changes in the indices recorded associated with the multiscale and chaotic structure of the tested object, which allows establishing short-and long-term structural and functional changes.Research aim was to reveal the dynamical peculiarities of interactions of cardiovascular system indices while evaluating the functional state of track-and-field athletes and Greco-Roman wrestlers.Research methods. Twenty two subjects participated in the study, their average age of 23.5 ± 1.7 years. During the study standard 12 lead electrocardiograms (ECG) were recorded. The following ECG parameters were used in the study: duration of RR interval taken from the II standard lead, duration of QRS complex, duration of JT interval and amplitude of ST segment taken from the V standard lead.Research  results.  Significant  differences  were  found  between  inter-parametric  connections  of  ST  segment amplitude and JT interval duration at the pre and post-training testing. Observed changes at different hierarchical levels of the body systems revealed inadequate cardiac metabolic processes, leading to changes in the metabolic rate of the myocardium and reflected in the dynamics of all investigated interactions.Discussion and conclusions. It has been found that peculiarities of the interactions of ECG indices interactions show the exposure of the  functional changes in the body at the onset of the workload. The alterations of the functional state of the body and the signs of fatigue, after athletes performed two high intensity training sessions per day, can be assessed using the approach of the evaluation of interactions between functional variables. Therefore the evaluation of the interactions of physiological signals by using time series analysis methods is suitable for the observation of these processes and the functional state of the body.Keywords: electrocardiogram, time series, functional state.

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