Power law scaling behavior of physiological time series in marathon races using wavelet leaders and detrended fluctuation analysis

2009 ◽  
Author(s):  
Eva Wesfreid ◽  
Véronique Billat
Keyword(s):  
Time Series ◽  
Power Law ◽  
Detrended Fluctuation Analysis ◽  
Scaling Behavior ◽  
Fluctuation Analysis ◽  
Physiological Time ◽  
Wavelet Leaders ◽  
Detrended Fluctuation

scholarly journals DETRENDED FLUCTUATION ANALYSIS OF THE TCP-RED ALGORITHM

2009 ◽  
Vol 19 (12) ◽  
pp. 4237-4245 ◽  
Author(s):  
XI CHEN ◽  
SIU-CHUNG WONG ◽  
CHI K. TSE ◽  
LJILJANA TRAJKOVIĆ
Keyword(s):  
Time Series ◽  
Long Range ◽  
Power Law ◽  
Detrended Fluctuation Analysis ◽  
Time Series Data ◽  
Analytical Models ◽  
Series Data ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Detrended Fluctuation

It has been observed that Internet gateways employing Transport Control Protocol (TCP) and the Random Early Detection (RED) control algorithm may exhibit instability and oscillatory behavior. Most control methods proposed in the past have been based on analytical models that rely on statistical measurements of network parameters. In this paper, we apply the detrended fluctuation analysis (DFA) method to analyze stability of the TCP-RED system. The DFA is used to analyze time-series data and generate power-law scaling exponents, which indicate the long-range correlations of the time series. We quantify the stability of the TCP-RED system by examining the variation of the DFA power-law scaling exponent when the system parameters are varied. We also study the long-range power-law correlations of TCP window periods.

Scaling analysis of hyperchaotic time series

2017 ◽  
Vol 28 (07) ◽  
pp. 1750094 ◽  
Author(s):  
J. S. Murguía
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Power Law ◽  
Detrended Fluctuation Analysis ◽  
Dynamical Evolution ◽  
Fluctuation Analysis ◽  
Discrete Wavelet ◽  
Scaling Analysis ◽  
Detrended Fluctuation ◽  
Hyperchaotic Systems

The time series of the states of several well-known hyperchaotic systems are analyzed numerically using the detrended fluctuation analysis based on the discrete wavelet transform. We report the finding of significant scaling behaviors (power-law like) in some of these time series, which can be used as an additional characteristic distinguishing the dynamical evolution of such systems.

FLUCTUATION ANALYSIS OF MONTHLY RAINFALL TIME SERIES

2010 ◽  
Vol 09 (02) ◽  
pp. 219-228 ◽  
Author(s):  
JORGE O. PIERINI ◽  
LUCIANO TELESCA
Keyword(s):  
Time Series ◽  
Detrended Fluctuation Analysis ◽  
Scaling Behavior ◽  
Monthly Rainfall ◽  
Fluctuation Analysis ◽  
Rainfall Time Series ◽  
Scaling Exponents ◽  
Times Series ◽  
Detrended Fluctuation

The monthly rainfall time series, spanning more than a century, recorded in several sites in the middle Argentina were analyzed. The power spetral density (PSD) method reveals the presence of annual and semi-annual cyclic fluctuations. The detrended fluctuation analysis (DFA) performed on the residual times series (after removing the periodicities) shows a scaling behavior, characterized by DFA scaling exponents ranging between 0.54 and 0.58. These findings could contribute to a better understanding of rainfall dynamics.

Investigation scaling behavior of earthquake-related signals in magnetotelluric measurements, Northern Algeria

2021 ◽  
Author(s):  
Ahmed Seddik Kasdi ◽  
Abderrezak Bouzid ◽  
Mohamed Hamoudi ◽  
Abdslem Abtout
Keyword(s):  
Time Series ◽  
Detrended Fluctuation Analysis ◽  
Seismic Event ◽  
Scaling Behavior ◽  
Stationary Time Series ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Time Dynamics ◽  
Electromagnetic Signals ◽  
Detrended Fluctuation

<p>The north-central region of Algeria has been characterized by a swarm-type seismicity, after the strong Mw6.8 Boumerdès earthquake of May 21, 2003, culminating with the earthquake that occurred on July 17, 2013 of magnitude Mw=5. A magnetotelluric station was installed on December 2014 in the Medea region, 60 km south of the capital Algiers. We measured the five components of the telluric and magnetic field with a sampling frequency of 15 Hz. The seismic activity in the region provided the opportunity to observe and study the earthquake’s related electromagnetic signal. The scaling properties of the recorded electric and magnetic time series were investigated. On the basis of multifractal detrended fluctuation analysis, which is a powerful method for detecting scaling in non-stationary time series, deviations from the uniform scale of the power law were identified and quantified. We investigated the time dynamics of the earthquake related electromagnetic time series measured at the magnetotelluric station. The multifractal detrended fluctuation analysis showed the different multifractality properties of electromagnetic signals before, during and after the seismic event. The results of this work show an unstable scaling behavior in electromagnetic data during the occurrence of the seismic event. These first results could be useful in the framework of seismo-electromagnetic signals studies.</p>

scholarly journals On the Validity of Detrended Fluctuation Analysis at Short Scales

Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 61
Author(s):  
Pedro Carpena ◽  
Manuel Gómez-Extremera ◽  
Pedro A. Bernaola-Galván
Keyword(s):  
Dynamical System ◽  
Time Series ◽  
Time Scales ◽  
Real World ◽  
Detrended Fluctuation Analysis ◽  
Scaling Behavior ◽  
Fluctuation Analysis ◽  
Scaling Properties ◽  
Short Scale ◽  
Detrended Fluctuation

Detrended Fluctuation Analysis (DFA) has become a standard method to quantify the correlations and scaling properties of real-world complex time series. For a given scale ℓ of observation, DFA provides the function F(ℓ), which quantifies the fluctuations of the time series around the local trend, which is substracted (detrended). If the time series exhibits scaling properties, then F(ℓ)∼ℓα asymptotically, and the scaling exponent α is typically estimated as the slope of a linear fitting in the logF(ℓ) vs. log(ℓ) plot. In this way, α measures the strength of the correlations and characterizes the underlying dynamical system. However, in many cases, and especially in a physiological time series, the scaling behavior is different at short and long scales, resulting in logF(ℓ) vs. log(ℓ) plots with two different slopes, α1 at short scales and α2 at large scales of observation. These two exponents are usually associated with the existence of different mechanisms that work at distinct time scales acting on the underlying dynamical system. Here, however, and since the power-law behavior of F(ℓ) is asymptotic, we question the use of α1 to characterize the correlations at short scales. To this end, we show first that, even for artificial time series with perfect scaling, i.e., with a single exponent α valid for all scales, DFA provides an α1 value that systematically overestimates the true exponent α. In addition, second, when artificial time series with two different scaling exponents at short and large scales are considered, the α1 value provided by DFA not only can severely underestimate or overestimate the true short-scale exponent, but also depends on the value of the large scale exponent. This behavior should prevent the use of α1 to describe the scaling properties at short scales: if DFA is used in two time series with the same scaling behavior at short scales but very different scaling properties at large scales, very different values of α1 will be obtained, although the short scale properties are identical. These artifacts may lead to wrong interpretations when analyzing real-world time series: on the one hand, for time series with truly perfect scaling, the spurious value of α1 could lead to wrongly thinking that there exists some specific mechanism acting only at short time scales in the dynamical system. On the other hand, for time series with true different scaling at short and large scales, the incorrect α1 value would not characterize properly the short scale behavior of the dynamical system.

scholarly journals AUTOMATICALLY DETECTING "SIGNIFICANT EVENTS" ON SenseCam

2013 ◽  
Vol 11 (06) ◽  
pp. 1350050 ◽  
Author(s):  
NA LI ◽  
MARTIN CRANE ◽  
HEATHER J. RUSKIN
Keyword(s):  
Time Series ◽  
Detrended Fluctuation Analysis ◽  
Time Windows ◽  
Fluctuation Analysis ◽  
Discrete Wavelet ◽  
Dominant Eigenvalue ◽  
Multiple Time ◽  
Correlation Relationship ◽  
Significant Events ◽  
Detrended Fluctuation

SenseCam is an effective memory-aid device that can automatically record images and other data from the wearer's whole day. The main issue is that, while SenseCam produces a sizeable collection of images over the time period, the vast quantity of captured data contains a large percentage of routine events, which are of little interest to review. In this article, the aim is to detect "Significant Events" for the wearers. We use several time series analysis methods such as Detrended Fluctuation Analysis (DFA), Eigenvalue dynamics and Wavelet Correlations to analyse the multiple time series generated by the SenseCam. We show that Detrended Fluctuation Analysis exposes a strong long-range correlation relationship in SenseCam collections. Maximum Overlap Discrete Wavelet Transform (MODWT) was used to calculate equal-time Correlation Matrices over different time scales and then explore the granularity of the largest eigenvalue and changes of the ratio of the sub-dominant eigenvalue spectrum dynamics over sliding time windows. By examination of the eigenspectrum, we show that these approaches enable detection of major events in the time SenseCam recording, with MODWT also providing useful insight on details of major events. We suggest that some wavelet scales (e.g., 8 minutes–16 minutes) have the potential to identify distinct events or activities.

Power-Law Fluctuation in Expressway Traffic Flow: Detrended Fluctuation Analysis

2006 ◽  
Vol 75 (3) ◽  
pp. 034002 ◽  
Author(s):  
S. Tadaki ◽  
M. Kikuchi ◽  
A. Nakayama ◽  
K. Nishinari ◽  
A. Shibata ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Power Law ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Detrended Fluctuation
Sign in / Sign up

Export Citation Format

Share Document