scholarly journals Fractal Analysis of BOLD Time Series in a Network Associated With Waiting Impulsivity

2018 ◽  
Vol 9 ◽  
Author(s):  
Atae Akhrif ◽  
Marcel Romanos ◽  
Katharina Domschke ◽  
Angelika Schmitt-Boehrer ◽  
Susanne Neufang
Keyword(s):  
Time Series ◽  
Fractal Analysis

Representative Time Series Fractal Analysis of the Traffic Flows of a Dedicated High-Speed Link

2021 ◽  
Author(s):  
Ginno Millan ◽  
manuel vargas ◽  
Guillermo Fuertes
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Long Range ◽  
Computer Networks ◽  
Fractal Analysis ◽  
High Speed ◽  
Long Range Dependence ◽  
Traffic Flows ◽  
Speed Computer

Fractal behavior and long-range dependence are widely observed in measurements and characterization of traffic flow in high-speed computer networks of different technologies and coverage levels. This paper presents the results obtained when applying fractal analysis techniques on a time series obtained from traffic captures coming from an application server connected to the internet through a high-speed link. The results obtained show that traffic flow in the dedicated high-speed network link exhibited fractal behavior since the Hurst exponent was in the range of 0.5, 1, the fractal dimension between 1, 1.5, and the correlation coefficient between -0.5, 0. Based on these results, it is ideal to characterize both the singularities of the fractal traffic and its impulsiveness during a fractal analysis of temporal scales. Finally, based on the results of the time series analyzes, the fact that the traffic flows of current computer networks exhibited fractal behavior with a long-range dependence was reaffirmed.

scholarly journals Fractal analysis of the time series of particulate material

2020 ◽  
Vol 1514 ◽  
pp. 012016
Author(s):  
D A Prada ◽  
D Parra ◽  
J D Tarazona ◽  
M F Silva ◽  
P Vera ◽  
...  
Keyword(s):  
Time Series ◽  
Fractal Analysis ◽  
Particulate Material

scholarly journals Use of fractal analysis to evaluate the surface quality of agricultural machinery parts

2020 ◽  
Vol 17 ◽  
pp. 00189
Author(s):  
Oleg Bavykin ◽  
Tatyana Levina ◽  
Vladlena Matrosova ◽  
Anatoly Klochkov ◽  
Vitaliy Enin
Keyword(s):  
Time Series ◽  
Computer Program ◽  
Surface Quality ◽  
Fractal Analysis ◽  
Hurst Exponent ◽  
High Accuracy ◽  
Agricultural Machinery ◽  
Fractal Characteristics

The research of the determination of the fractal characteristics of the surface of a material proposes the use of a stationary profilograph and a computer program for calculating the Hurst exponent. The low accuracy of fractal analysis using the well-known computer program Fractan is revealed. A computer program developed in VBA for the fractal analysis of the time series is described. The high accuracy of the algorithms for calculating the Hurst exponent incorporated in this program is shown.

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

scholarly journals Fractal Scaling Properties in Rainfall Time Series: A Case of Thiruvallur District, Tamil Nadu, India

2021 ◽  
Author(s):  
Ibrahim Lawal Kane ◽  
Venkatesan Madha Suresh
Keyword(s):  
Time Series ◽  
Fractal Analysis ◽  
Tamil Nadu ◽  
Structure Design ◽  
Rainfall Time Series ◽  
Scaling Properties ◽  
Future Data ◽  
Data Point ◽  
Core Idea ◽  
Red Hills

In the present study, the features of rainfall time series (1971–2016) in 9 meteorological regions of Thiruvallur, Tamil Nadu, India that comprises Thiruvallur, Korattur_Dam, Ponneri, Poondi, Red Hills, Sholingur, Thamaraipakkam, Thiruvottiyur and Vallur Anicut were studied. The evaluation of rainfall time series is one of the approaches for efficient hydrological structure design. Characterising and identifying patterns is one of the main objectives of time series analysis. Rainfall is a complex phenomenon, and the temporal variation of this natural phenomenon has been difficult to characterise and quantify due to its randomness. Such dynamical behaviours are present in multiple domains and it is therefore essential to have tools to model them. To solve this problem, fractal analysis based on Detrended Fluctuation Analysis (DFA) and Rescaled Range (R/S) analysis were employed. The fractal analysis produces estimates of the magnitude of detrended fluctuations at different scales (window sizes) of a time series and assesses the scaling relationship between estimates and time scales. The DFA and (R/S) gives an estimate known as Hurst exponent (H) that assumes self-similarity in the time series. The results of H exponent reveals typical behaviours shown by all the rainfall time series, Thiruvallur and Sholingur rainfall region have H exponent values within 0.5 < H < 1 which is an indication of persistent behaviour or long memory. In this case, a future data point is likely to be followed by a data point preceding it; Ponneri and Poondi have conflicting results based on the two methods, however, their H values are approximately 0.5 showing random walk behaviour in which there is no correlation between any part and a future. Thamaraipakkam, Thiruvottiyur, Vallur Anicut, Korattur Dam and Red Hills have H values less than 0.5 indicating a property called anti-persistent in which an increase will tend to be followed by a decrease or vice versa. Taking into consideration of such features in modelling, rainfall time series could be an exhaustive rainfall model. Finding appropriate models to estimate and predict future rainfalls is the core idea of this study for future research.

scholarly journals Fractal analysis of muscle activity patterns during locomotion: pitfalls and how to avoid them

2020 ◽  
Author(s):  
Alessandro Santuz ◽  
Turgay Akay
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Fractal Analysis ◽  
Hurst Exponent ◽  
Activity Patterns ◽  
Surrogate Data ◽  
Time Dependent ◽  
Physiological Data ◽  
Adult Mice

AbstractTime-dependent physiological data, such as electromyogram (EMG) recordings from multiple muscles, is often difficult to interpret objectively. Here, we used EMG data gathered during mouse locomotion to investigate the effects of calculation parameters and data quality on two metrics for fractal analysis: the Higuchi’s fractal dimension (HFD) and the Hurst exponent (H). A curve is fractal if it repeats itself at every scale or, in other words, if its shape remains unchanged when zooming in the curve at every zoom level. Many linear and nonlinear analysis methods are available, each of them aiming at the explanation of different data features. In recent years, fractal analysis has become a powerful nonlinear tool to extract information from physiological data not visible to the naked eye. It can present, however, some dangerous pitfalls that can lead to misleading interpretations. To calculate the HFD and the H, we have extracted muscle synergies from normal and mechanically perturbed treadmill locomotion from the hindlimb of adult mice. Then, we used one set per condition (normal and perturbed walking) of the obtained time-dependent coefficients to create surrogate data with different fluctuations over the original mean signal. Our analysis shows that HFD and H are exceptionally sensitive to the presence or absence of perturbations to locomotion. However, both metrics suffer from variations in their value depending on the parameters used for calculations and the presence of quasi-periodic elements in the time series. We discuss those issues giving some simple suggestions to reduce the chance of misinterpreting the outcomes.New & NoteworthyDespite the lack of consensus on how to perform fractal analysis of physiological time series, many studies rely on this technique. Here, we shed light on the potential pitfalls of using the Higuchi’s fractal dimension and the Hurst exponent. We expose and suggest how to solve the drawbacks of such methods when applied to data from normal and perturbed locomotion by combining in vivo recordings and computational approaches.

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