scholarly journals Long-range correlation and nonlinearity in geochemical time series of gas discharges from Mt. Etna, and changes with 2001 and 2002–2003 eruptions

2010 ◽  
Vol 17 (6) ◽  
pp. 733-751 ◽  
Author(s):  
A. Paonita

Abstract. In this paper, spectral and detrended fluctuation analyses, as well as time reversibility and magnitude-sign decomposition, have been applied to the 10-year time-series data resulting from geochemical monitoring of gas emissions on the flanks of Mt. Etna, and gases from a CO2 exploitation well located tens of kilometers from the volcano. The analysis of the time series which showed main effects of fractionation between gases due to selective dissolution in aquifers (e.g., the CO2 concentration series), revealed the occurrence of random fluctuations in time, typical of systems where several processes combine linearly. In contrast, the series of He isotopic composition exhibited power-law behavior of the second-order fluctuation statistics, with values of the scaling exponent close to 0.9. When related to the spectral exponent, this value indicates that the isotopic series closely resemble fractal flicker-noise signals having persistent long-range correlations. The isotopic signals also displayed asymmetry under time reversal and long-range correlation of the associated magnitude series, therefore it was statistically proved the presence of nonlinearity. Both long-range correlation and nonlinearity in time series have been generally considered as distinctive features of dynamic systems where numerous processes interact by feedback mechanisms, in accordance with the paradigm of self-organized criticality (SOC). Thus, it is here proposed that the system that generated the isotope series worked under conditions of SOC. Since the fluctuations of the isotope series have been related to magma degassing, the previous results place constraints on the dynamics of such process, and suggest that nonequilibrium conditions must be dominant. It remains unclear whether the signature of SOC is directly due to volatile degassing from magma, or if it derives from the interaction between melt and the stress field, which certainly influences magma decompression. The strength of scaling appears to increase after 2002 (α values from 0.8 up to 1.2), focusing on transition of the Etnean system from typical SOC toward conditions of lower criticality. By comparing this transition with those of geophysical observables, it can be suggested that the drop in the rate of magma supply, subsequent to the paroxysms of 2001 and 2002–2003, was the main cause of the scaling change.

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 441 ◽  
Author(s):  
Maria C. Mariani ◽  
Peter K. Asante ◽  
Md Al Masum Bhuiyan ◽  
Maria P. Beccar-Varela ◽  
Sebastian Jaroszewicz ◽  
...  

In this study, we use the Diffusion Entropy Analysis (DEA) to analyze and detect the scaling properties of time series from both emerging and well established markets as well as volcanic eruptions recorded by a seismic station, both financial and volcanic time series data have high frequencies. The objective is to determine whether they follow a Gaussian or Lévy distribution, as well as establish the existence of long-range correlations in these time series. The results obtained from the DEA technique are compared with the Hurst R/S analysis and Detrended Fluctuation Analysis (DFA) methodologies. We conclude that these methodologies are effective in classifying the high frequency financial indices and volcanic eruption data—the financial time series can be characterized by a Lévy walk while the volcanic time series is characterized by a Lévy flight.


2009 ◽  
Vol 19 (12) ◽  
pp. 4237-4245 ◽  
Author(s):  
XI CHEN ◽  
SIU-CHUNG WONG ◽  
CHI K. TSE ◽  
LJILJANA TRAJKOVIĆ

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.


2013 ◽  
Vol 80 (1) ◽  
pp. 43-58 ◽  
Author(s):  
M. Lafouti ◽  
M. Ghoranneviss ◽  
S. Meshkani ◽  
A. Salar Elahi

Tokamak edge plasma was analyzed by applying the multifractal detrend fluctuation analysis (MF-DFA) technique. This method has found wide application in the analysis of correlations and characterization of scaling behavior of the time-series data in physiology, finance, and natural sciences. The time evolution of the ion saturation current (Is), the floating potential fluctuation (Vf), the poloidal electric field (Ep), and the radial particle flux (Γr) has been measured by using a set of Langmuir probes consisting of four tips on the probe head. The generalized Hurst exponents (h(q)), local fluctuation function (Fq(s)), the Rényi exponents (τ(q)) as well as the multifractal spectrum f(αh) have been calculated by applying the MF-DFA method to Is, Vf, and the magnetohydrodynamic (MHD) fluctuation signal. Furthermore, we perform the shuffling and the phase randomization techniques to detect the sources of multifractality. The nonlinearity shape of τ(q) reveals a multifractal behavior of the time-series data. The results show that in the presence of biasing, Is, Vf, Ep, and Γr reduce about 25%, 90%, 70%, and 50%, respectively, compared with the situation with no biasing. Also, they reduce about 15%, 90%, 35%, and 25%, respectively, after resonant helical magnetic field (RHF) application. In the presence of biasing or RHF, the amplitude of the power spectrum of Is, Vf, Γr, and MHD activity reduce remarkably in all the ranges of frequency, while their h(q) increase. The values of h(q) have been restricted between 0.6 and 0.68. These results are evidence of the existence of long-range correlations in the plasma edge turbulence. They also show the self-similar nature of the plasma edge fluctuations. Biasing or RHF reduces the amount of Fq(s). The multifractal spectrum width of Is, Vf, and MHD fluctuation amplitude reduce about 60%, 70%, and 42%, respectively, by applying biasing. In the presence of RHF, their width reduces about 60%, 85%, and 75%, respectively. It means that biasing and RHF reduce the degree of multifractality.


2007 ◽  
Vol 72 (4) ◽  
pp. 383-392 ◽  
Author(s):  
Adriana Isvoran ◽  
Laura Unipan ◽  
Dana Craciun ◽  
Vasile Morariu

The results presented here suggest the existence of correlations in the sequence data of proteins. 32 proteins, both globular and fibrous, both monomeric and polymeric, were analyzed. The primary structures of these proteins were treated as time series. Three spatial series of data for each sequence of a protein were generated from numerical correspondences between each amino acid and a physical property associated with it, i.e., its electric charge, its polar character and its dipole moment. For each series, the spectral coefficient, the scaling exponent and the Hurst coefficient were determined. The values obtained for these coefficients revealed non-randomness in the series of data. .


2008 ◽  
Vol 19 (04) ◽  
pp. 665-669 ◽  
Author(s):  
A. N. S. FILHO ◽  
G. F. ZEBENDE ◽  
M. A. MORET

The transportation problems are in evidence due to their importance. This type of problem is usually studied by nonlinear dynamics. In this paper, we study time series of vehicle demand by using the ferry-boat system between Salvador city and Itaparica island, in Bahia, Brazil. We observe an interesting behavior since these time series allow to observe genuine self-affinity effects. The scaling exponent α and the density of crossing points ρ are variables to describe long-range correlations (self-affinity) in time series of the vehicle demand. As the result we show α and ρ variation year by year.


2016 ◽  
Vol 27 (12) ◽  
pp. 1650143 ◽  
Author(s):  
S. M. Azevedo ◽  
H. Saba ◽  
J. G. V. Miranda ◽  
A. S. Nascimento Filho ◽  
M. A. Moret

Dengue is a complex public health problem that is common in tropical and subtropical regions. This disease has risen substantially in the last three decades, and the physical symptoms depict the self-affine behavior of the occurrences of reported dengue cases in Bahia, Brazil. This study uses detrended fluctuation analysis (DFA) to verify the scale behavior in a time series of dengue cases and to evaluate the long-range correlations that are characterized by the power law [Formula: see text] exponent for different cities in Bahia, Brazil. The scaling exponent ([Formula: see text]) presents different long-range correlations, i.e. uncorrelated, anti-persistent, persistent and diffusive behaviors. The long-range correlations highlight the complex behavior of the time series of this disease. The findings show that there are two distinct types of scale behavior. In the first behavior, the time series presents a persistent [Formula: see text] exponent for a one-month period. For large periods, the time series signal approaches subdiffusive behavior. The hypothesis of the long-range correlations in the time series of the occurrences of reported dengue cases was validated. The observed self-affinity is useful as a forecasting tool for future periods through extrapolation of the [Formula: see text] exponent behavior. This complex system has a higher predictability in a relatively short time (approximately one month), and it suggests a new tool in epidemiological control strategies. However, predictions for large periods using DFA are hidden by the subdiffusive behavior.


2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston

Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.


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