Dimension of the minimal cover and fractal analysis of time series

2004 ◽  
Vol 339 (3-4) ◽  
pp. 591-608 ◽  
Author(s):  
M.M Dubovikov ◽  
N.V Starchenko ◽  
M.S Dubovikov
Keyword(s):  
Time Series ◽  
Fractal Analysis ◽  
Minimal Cover ◽  
Analysis Of Time Series

Fractal analysis of time series in oil and gas production

2009 ◽  
Vol 41 (5) ◽  
pp. 2474-2483 ◽  
Author(s):  
Arif A. Suleymanov ◽  
Askar A. Abbasov ◽  
Aydin J. Ismaylov
Keyword(s):  
Time Series ◽  
Fractal Analysis ◽  
Oil And Gas ◽  
Gas Production ◽  
Oil And Gas Production ◽  
Analysis Of Time Series

THE ROBUST FRACTAL ANALYSIS OF TIME SERIES: CONCERNING SIGNAL CLASS AND DATA LENGTH

Fractals ◽  
2011 ◽  
Vol 19 (01) ◽  
pp. 29-49 ◽  
Author(s):  
M. H. FATTAHI ◽  
N. TALEBBEYDOKHTI ◽  
G. R. RAKHSHANDEHROO ◽  
A. SHAMSAI ◽  
E. NIKOOEE
Keyword(s):  
Time Series ◽  
Fractal Analysis ◽  
Variation Method ◽  
Fluctuation Analysis ◽  
Analysis Method ◽  
Range Analysis ◽  
Rescaled Range Analysis ◽  
Data Points ◽  
Data Length ◽  
Analysis Of Time Series

In the present paper, the influence of the signal class (fBm/fGn) and the data length of time series on choosing the robust fractal analysis method have been studied. More than 1000 fBm/fGn generated time series in short, intermediate and long ranges have been analyzed using common fractal analysis methods. The chosen techniques were power spectral density, detrended fluctuation analysis, rescaled range analysis, box counting, average wavelet coefficients, and the variation method. Numerous graphs indicating the suitability of each method in terms of biases in calculating the fundamental fractal feature of time series, Hurst coefficient, were employed. The results strongly emphasized the crucial influence of the signal class as well as the data length when choosing the appropriate fractal analysis method. Furthermore, as a step forward, a study on the number of data points present in a classified class/length was performed. The effect of the number of data points could not be neglected either. Based on the results, a strategy flowchart for fractal analysis of time series has been proposed. Finally, as an empirical example, the monthly, weekly and daily scaled flow time series of Ghar-e-Aghaj River have been analyzed within the framework of the strategy flowchart.

scholarly journals A GAME MODEL OF SUPPLY CHAIN MANAGEMENT BASED ON FRACTAL ANALYSIS OF TIME SERIES

Fractals ◽  
2020 ◽  
Vol 28 (08) ◽  
pp. 2040023
Author(s):  
SHANGHONG LI ◽  
LIANG LIAO ◽  
SHENG-HUNG CHANG
Keyword(s):  
Time Series ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Fractal Analysis ◽  
Nonlinear Problems ◽  
Current Status ◽  
Prediction Algorithm ◽  
Game Model ◽  
Chain Management ◽  
Analysis Of Time Series

Fractal analysis of time series is an important tool for describing complex systems and solving nonlinear problems based on time series datebases, of which the core is believed that the geometric bodies composed of various parts of the system have self-similarity and scale invariance. Supply chain management uses integrated thinking, from the perspective of system, to design, plan and control the logistics, capital flow, and information flow in the supply chain for minimizing internal consumption, improving competitiveness or welfare levels, and achieving win–win cooperation. Whether or not the supply chain enterprises cooperate is essentially a game between the enterprises; according to the game theory, the profit of an enterprise depends not only on its own behavior, but also on the behavior of another enterprise with which it deals. On basis of summarizing and analyzing of previous research works, this paper expounded the current research status and significance of the game model of supply chain management, elaborated the development background, current status, and future challenges of the fractal analysis of time series, introduced the methods and principles of fractal dimension calculation and replicator dynamic equation, established a game model of supply chain management based on the fractal analysis of time series including scale-free area determination and model optimization solution, and conducted the fractal analysis of the game model of supply chain management and discussed the equilibrium and statistical similarity of this proposed model. The final simulation experiment showed that the game model of supply chain management based on the fractal analysis of time series applied the phase space reconstruction principle to the data sample selection of fractal prediction algorithm in supply chain, which solved the problem of low similarity between data samples taken by fractal prediction objects with fuzzy self-similar characteristics and periodic ambiguity, and improved the prediction accuracy of the fractal prediction algorithm when predicting objects in the game model of supply chain management.

scholarly journals A Study on Fractal Analysis of Time Series

1996 ◽  
Vol 32 (11) ◽  
pp. 1569-1571
Author(s):  
Masaya SEKIMOTO ◽  
Takehiko HORITA ◽  
Sumitoshi OGATA
Keyword(s):  
Time Series ◽  
Fractal Analysis ◽  
Analysis Of Time Series

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.

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