scholarly journals Review of modern models and methods of analysis of time series of dynamics of processes in social, economic and socio-technical systems

2020 ◽  
Vol 8 (4) ◽  
pp. 7-45
Author(s):  
E. G. Andrianova ◽  
S. A. Golovin ◽  
S. V. Zykov ◽  
S. A. Lesko ◽  
E. R. Chukalina
Keyword(s):  
Social Networks ◽  
Time Series ◽  
Complex Systems ◽  
Human Factor ◽  
General Information ◽  
Commodity Markets ◽  
Measurement Scale ◽  
Advantages And Disadvantages ◽  
Technical Systems ◽  
Analysis Of Time Series

The directions of perspective research in the field of analysis and modeling of the dynamics of time series of processes in complex systems with the presence of the human factor are described. The dynamics of processes in such systems is described by nonstationary time series. Predicting the evolution of such systems is of great importance for managing processes in social (election campaigns), economic (stock, futures and commodity markets) and socio-technical systems (social networks). The general information on time series and tasks of their analysis is given. Modern methods of time series analysis for economic processes are considered. The results show that economic processes cannot be considered completely random, since they tend to self-organize and, moreover, are subject to the influence of memory of previous states. It was revealed that one of the main tasks in modeling processes in sociotechnical systems (for example, social networks) is the development of a mathematical apparatus for bringing data to a single measurement scale. The modern models of analysis and forecasting of electoral processes based on the analysis of time series: structural, polling, hybrid. Based on the analysis, their advantages and disadvantages are considered. In conclusion, it was concluded that to describe processes in complex systems with the presence of the human factor, in addition to traditional factors, it is necessary to develop and use methods and tools to take into account the possibility of self-organization of human groups and the presence of memory about previous states of the system.

Diagnosis and Evaluation

2017 ◽  
pp. 138-181
Author(s):  
Yury N. Kovalyov
Keyword(s):  
Decision Making ◽  
Complex Systems ◽  
Human Activity ◽  
Model Theory ◽  
Human Factor ◽  
Open Systems ◽  
Wave Model ◽  
Mathematical Apparatus ◽  
Accident Rate ◽  
Technical Systems

The accident free work of complex systems depends of the compatibility of their components. When it comes to socio-technical, this means the compatibility of the human factor with the environment and equipment, organized through a specific interface. At the same time, there is a certain contradiction: the modeling and design of equipment and interface is based on a classical mathematical apparatus, whereas its use for understanding human activity is confronted with the non-formalizability of many aspects of perception and decision-making. Elimination of this contradiction on the basis of the modeling apparatus, equally suitable for modeling all components of socio-technical systems, will open the way to improving the compatibility of components and reducing the accident rate. Therefore, the development of such a mathematical apparatus is an important problem. In this chapter is presented the modelling instrument, which is adequate to the composite open systems properties – axiomatic wave model, theory of self-organization, practical examples.

Diagnosis and Evaluation

2021 ◽  
pp. 1341-1372
Author(s):  
Yury N. Kovalyov
Keyword(s):  
Decision Making ◽  
Complex Systems ◽  
Human Activity ◽  
Model Theory ◽  
Human Factor ◽  
Open Systems ◽  
Wave Model ◽  
Mathematical Apparatus ◽  
Accident Rate ◽  
Technical Systems

The accident free work of complex systems depends of the compatibility of their components. When it comes to socio-technical, this means the compatibility of the human factor with the environment and equipment, organized through a specific interface. At the same time, there is a certain contradiction: the modeling and design of equipment and interface is based on a classical mathematical apparatus, whereas its use for understanding human activity is confronted with the non-formalizability of many aspects of perception and decision-making. Elimination of this contradiction on the basis of the modeling apparatus, equally suitable for modeling all components of socio-technical systems, will open the way to improving the compatibility of components and reducing the accident rate. Therefore, the development of such a mathematical apparatus is an important problem. In this chapter is presented the modelling instrument, which is adequate to the composite open systems properties – axiomatic wave model, theory of self-organization, practical examples.

Correlation in the heart rate data

1998 ◽  
Vol 2 ◽  
pp. 141-148
Author(s):  
J. Ulbikas ◽  
A. Čenys ◽  
D. Žemaitytė ◽  
G. Varoneckas
Keyword(s):  
Nonlinear Dynamics ◽  
Experimental Data ◽  
Heart Rate ◽  
Time Series ◽  
Cardiovascular Function ◽  
Statistical Properties ◽  
Rate Data ◽  
Heart Rate Data ◽  
Dynamical Nature ◽  
Analysis Of Time Series

Variety of methods of nonlinear dynamics have been used for possibility of an analysis of time series in experimental physiology. Dynamical nature of experimental data was checked using specific methods. Statistical properties of the heart rate have been investigated. Correlation between of cardiovascular function and statistical properties of both, heart rate and stroke volume, have been analyzed. Possibility to use a data from correlations in heart rate for monitoring of cardiovascular function was discussed.

scholarly journals Interpretation of the Chemical and Physical Time-Series Retrieved from Sentik Glacier, Ladakh Himalaya, India

1984 ◽  
Vol 30 (104) ◽  
pp. 66-76 ◽  
Author(s):  
Paul A. Mayewski ◽  
W. Berry Lyons ◽  
N. Ahmad ◽  
Gordon Smith ◽  
M. Pourchet
Keyword(s):  
Time Series ◽  
Chemical Species ◽  
Data Sets ◽  
Reactive Iron ◽  
Physical Time ◽  
Ladakh Himalaya ◽  
Data Density ◽  
Mass Circulation ◽  
The Himalaya ◽  
Analysis Of Time Series

AbstractSpectral analysis of time series of a c. 17 ± 0.3 year core, calibrated for total ß activity recovered from Sentik Glacier (4908m) Ladakh, Himalaya, yields several recognizable periodicities including subannual, annual, and multi-annual. The time-series, include both chemical data (chloride, sodium, reactive iron, reactive silicate, reactive phosphate, ammonium, δD, δ(18O) and pH) and physical data (density, debris and ice-band locations, and microparticles in size grades 0.50 to 12.70 μm). Source areas for chemical species investigated and general air-mass circulation defined from chemical and physical time-series are discussed to demonstrate the potential of such studies in the development of paleometeorological data sets from remote high-alpine glacierized sites such as the Himalaya.

scholarly journals Evaluating the performance of multivariate indicators of resilience loss

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Els Weinans ◽  
Rick Quax ◽  
Egbert H. van Nes ◽  
Ingrid A. van de Leemput
Keyword(s):  
Time Series ◽  
Complex Systems ◽  
Multivariate Time Series ◽  
Tipping Points ◽  
Physical And Mental Health ◽  
One Dimensional ◽  
Slowing Down ◽  
The Past ◽  
Technological Developments ◽  
Resilience Indicators

AbstractVarious complex systems, such as the climate, ecosystems, and physical and mental health can show large shifts in response to small changes in their environment. These ‘tipping points’ are notoriously hard to predict based on trends. However, in the past 20 years several indicators pointing to a loss of resilience have been developed. These indicators use fluctuations in time series to detect critical slowing down preceding a tipping point. Most of the existing indicators are based on models of one-dimensional systems. However, complex systems generally consist of multiple interacting entities. Moreover, because of technological developments and wearables, multivariate time series are becoming increasingly available in different fields of science. In order to apply the framework of resilience indicators to multivariate time series, various extensions have been proposed. Not all multivariate indicators have been tested for the same types of systems and therefore a systematic comparison between the methods is lacking. Here, we evaluate the performance of the different multivariate indicators of resilience loss in different scenarios. We show that there is not one method outperforming the others. Instead, which method is best to use depends on the type of scenario the system is subject to. We propose a set of guidelines to help future users choose which multivariate indicator of resilience is best to use for their particular system.

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