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Background Understanding the underlying architecture of mood regulation in bipolar disorder (BD) is important, as we are starting to conceptualize BD as a more complex disorder than one of recurring manic or depressive episodes. Nonlinear techniques are employed to understand and model the behavior of complex systems. Our aim was to assess the underlying nonlinear properties that account for mood and energy fluctuations in patients with BD; and to compare whether these processes were different in healthy controls (HC) and unaffected first-degree relatives (FDR). We used three different nonlinear techniques: Lyapunov exponent, detrended fluctuation analysis and fractal dimension to assess the underlying behavior of mood and energy fluctuations in all groups; and subsequently to assess whether these arise from different processes in each of these groups. Results There was a positive, short-term autocorrelation for both mood and energy series in all three groups. In the mood series, the largest Lyapunov exponent was found in HC (1.84), compared to BD (1.63) and FDR (1.71) groups [F (2, 87) = 8.42, p Conclusions The underlying nature of mood variability is in keeping with that of a chaotic system, which means that fluctuations are generated by deterministic nonlinear process(es) in HC, BD, and FDR. The value of this complex modeling lies in analyzing the nature of the processes involved in mood regulation. It also suggests that the window for episode prediction in BD will be inevitably short.

Present study is aimed at investigating the solar faculae area from 1990 to 2007 which partially covered the22nd and 23rd solar cycle. Rescaled Range Analysis (RRA) and Detrended Fluctuation Analysis (DFA) have beenadopted to evaluate the behaviour of nonlinear dynamics of solar faculae area. Results show that the value of Hurstexponent for solar faculae area from RRA and DFA is negatively correlated. It means it is non-persistent and longrange correlated. Obtained result is inaccurate so the only solution is to transform the data into stationary data by takingdifferencing. RRA is applied on residuals and RRA to evaluate the fractal property of the time series. Solar faculae areainvestigated in this study is fractal in nature and predictable ass well. Moreover, the time series of solar faculae area isnon-linear as established by the Brock – Dechert – Scheinkman (BDS) test results

The Nagel–Schreckenberg vehicular traffic model is extended to analyse a system of vehicles moving on a scale-free network to study extreme events in them. The dependence of the free-flow and congestion states on the density of vehicles is determined. In particular, a free-flow state at low vehicular density is observed and it gradually transitions to a congested state at higher density. Using detrended fluctuation analysis, it is shown that the flux of vehicles are long range correlated in a regime of low density. At higher densities, the flux becomes uncorrelated. We study the recurrence interval distribution for extreme events and the probability for its occurrence at low and high vehicular density regimes. It is shown that the occurrence probability for extreme events is independent of the degree of the node or the threshold used for defining extreme events

Studies on entomopter’s performance under the influence of gust have received impetus in the past decade. There exists a dire need to ascertain the threshold of the frontal gusty conditions which would destabilize these anthropogenic flyers. This would help to devise methods to mitigate the detrimental effects of gust. In light of this aspect, the present study aims at analyzing the onsets of instability in a flapping wing system subjected to temporal gust by employing recurrence period density entropy (RPDE) and detrended fluctuation analysis (DFA). Simulation of the flapping wing along inclined stroke is carried out for a Reynolds number of 150. This Reynolds number lies in the typical operating regime of fruit flies and entomopters like the Pico aerial vehicle. Numerical simulations are carried out to solve the laminar, unsteady, and incompressible Navier–Stokes equations. The dynamic meshing technique is employed to model flapping kinematics. Nine gusts with a combination of frequency and velocity ratios of 0.1, 0.5, and 1.0 are considered. Instantaneous horizontal and vertical forces are estimated. Time series of these forces are analyzed using RPDE and DFA paradigms. These analyses indicate that gust frequency of an order of magnitude higher than flapping frequency and gust amplitude of the order of magnitude as the wing’s root mean square velocity induces a possible onset of instability.

Purpose The new COVID-19 pandemic caused by SARS-CoV-2 initiated in the world a large quarantine, due to the exponential capacity of the virus in spreading from human contact. In the present work, the dynamics of such spreading was evaluated by the analysis of the growth factor of the disease. Method It was applied the space phase of the time series, the detrended fluctuation analysis (DFA) of the series, and the fractal dimension of the space phase. Results It was possible to notice a strange attractor in the space phase of the growth factor indicating that the process is a deterministic chaos. The value of the alpha coefficient by DFA showed to be less than 0.5, characteristic of anti-persistent long-range memory, in which large events alternate with small events, and vice-versa. The fractal dimension of the phase space set was a fractal number, between 1 and 2, another indicator that the growth factor of the disease is not random. Conclusion This global analysis is pointing that the spread of COVID-19 is a deterministic chaos, with long-range memory. Understanding such dynamics may help the control of this disease.

A nonvibrating magnetic granular system is studied by using a time series approach. The system consists of steel balls confined inside a circular wall that surrounds a glass plate. Kinetic energy is provided to the particles by the application of an external vertical time-dependent magnetic field of different amplitudes. We carried out a characterization of the system dynamics through the measurement of the correlations present in the time series of positions, in the x-direction, of each particle. In particular, by performing Fourier spectral analysis, we find that the time series are fractal and scale invariant, in such a way that the corresponding Fourier power spectra follow a power law $$P(f)\propto 1/f^\beta$$P(f)∝1/fβ, with $$00<β<2.5. More specifically, we find that the values of $$\beta$$β, and therefore the strength of the correlations, increase as the magnetic field also increases. In this way, the present system constitutes an experimental model to generate correlated random walks. Additionally, we show how the introduction of a constant magnetic field breaks down this scale invariance property in the positions of each particle. Finally, we confirm the above results by applying detrended fluctuation analysis.

The robustness of two widespread multifractal analysis methods, one based on detrended fluctuation analysis and one on wavelet leaders, is discussed in the context of time-series containing non-uniform structures with only isolated singularities. Signals generated by simulated and experimentally-realized chaos generators, together with synthetic data addressing particular aspects, are taken into consideration. The results reveal essential limitations affecting the ability of both methods to correctly infer the non-multifractal nature of signals devoid of a cascade-like hierarchy of singularities. Namely, signals harboring only isolated singularities are found to artefactually give rise to broad multifractal spectra, resembling those expected in the presence of a well-developed underlying multifractal structure. Hence, there is a real risk of incorrectly inferring multifractality due to isolated singularities. The careful consideration of local scaling properties and the distribution of H\"older exponent obtained, for example, through wavelet analysis, is indispensable for rigorously assessing the presence or absence of multifractality., Comment: To appear in Nonlinear Dynamics

We consider a method of detrended fluctuation analysis (DFA method), which enables the study of long-range correlations in nonstationary processes. Its modification is proposed, including the calculation of additional quantity, that is, a scaling index, characterizing the effects of nonstationarity in the experimental data. Using the dynamics of the blood flow velocity in cerebral vessels as an example, the possibilities of a quantitative description of changes in the signal structure using the proposed modification of the DFA method have been demonstrated.

International audience; The detrended fluctuation analysis (DFA) and its variants are popular methods to analyze the self-similarity of a signal. Two steps characterize them: firstly, the trend of the centered integrated signal is estimated and removed. Secondly, the properties of the so-called fluctuation function which is an approximation of the standard deviation of the resulting process is analyzed. However, it appears that the statistical mean was assumed to be equal to zero to obtain it. As there is no guarantee that this assumption is true a priori, this hypothesis is debatable. The purpose of this paper is to propose two alternative definitions of the fluctuation function. Then, we compare all of them based on a matrix formulation and the filter-based interpretation we recently proposed. This analysis will be useful to show that the approach proposed in the original paper remains a good compromise in terms of accuracy and computational cost.

The detrended fluctuation analysis (DFA) is extensively useful in stochastic processes to unveil the long-term correlation. Here, we apply the DFA to point processes that mimick earthquake data. The point processes are synthesized by a model similar to the Epidemic-Type Aftershock Sequence model, and we apply the DFA to time series $N(t)$ of the point processes, where $N(t)$ is the cumulative number of events up to time $t$. Crossover phenomena are found in the DFA for these time series, and extensive numerical simulations suggest that the crossover phenomena are signatures of non-stationarity in the time series. We also find that the crossover time represents a characteristic time scale of the non-stationary process embedded in the time series. Therefore, the DFA for point processes is especially useful in extracting information of non-stationary processes when time series are superpositions of stationary and non-stationary signals. Furthermore, we apply the DFA to the cumulative number $N(t)$ of real earthquakes in Japan, and we find a crossover phenomenon similar to that found for the synthesized data., Comment: 9 pages, 5 figures

The aim of this paper is to combine the scale-space method and the method of two-dimensional detrended fluctuation analysis to study the correlation properties of surfaces of self-organizing structures. A description of the proposed method for studying the correlation properties of the surface is given. Model surfaces are used to demonstrate operation of the scale-space method. The results of studying porous silicon films obtained using the developed procedure are given.

Based on multi-fractal detrended fluctuation analysis (MF-DFA) method, the extended self-similarity (ESS) is incorporated to develop a novel method to quantify multi-fractal characteristics. Detailed results show that compared with MF-DFA this new extended self-similarity based MF-DFA (ESS-MF-DFA) method can significantly extend scaling range and reduce uncertainties in estimating the exponents. Moreover, although ESS-MF-DFA method is developed from the DFA method with a fundamental assumption of a definite scaling range between fluctuation function and scale, ESS-MF-DFA can still work well even when DFA fails due to no scaling range between fluctuation function and scale. Furthermore, a criterion without estimating the generalized Hurst exponents is developed based on ESS-MF-DFA to distinguish multi-fractal from mono-fractal behavior and to quantify multi-fractal strength. All these results indicate that ESS-MF-DFA outperforms MF-DFA in reliably handling multi-fractal quantifications for much wider fields.

Stochastic fractals of the 1/f noise type are an important manifestation of the brain’s electrical activity and other real-world complex systems. Fractal complexity can be successfully estimated by methods such as the Higuchi method and detrended fluctuation analysis (DFA). In this study, we show that if, as with the EEG, the signal is a combination of fractal and oscillation, the estimates of fractal characteristics will be inaccurate. On our test data, DFA overestimated the fractal dimension, while the Higuchi method led to underestimation in the presence of high-amplitude, densely sampled oscillations.

Extended detrended fluctuation analysis (EDFA) is a recently proposed modification of the conventional method, which provides a characterization of complex time series with varying nonstationarity. It evaluates two scaling exponents for a better quantification of inhomogeneous datasets. Here, we study the effect of different types of nonstationarity on these exponents, including trend, switching between processes with distinct statistical properties and energy variability. Using the simulated signals, we show that the first two types of nonstationarity have the strongest effect for anticorrelated processes and complicate their diagnosis. Nonstationarity in energy is more crucial for time series with positive long-range correlations. Next, we apply EDFA to rat experiments to study the activation of brain lymphatic drainage during sleep. Our analysis reveals significant distinctions in EDFA’s measures between the background electrical activity of the brain and the stage of sleep. The latter offers an indirect way to identify and characterize the nightly activation of the drainage and clearance of brain tissue.

Wind farms can be regarded as complex systems that are, on the one hand, coupled to the nonlinear, stochastic characteristics of weather and, on the other hand, strongly influenced by supervisory control mechanisms. One crucial problem in this context today is the predictability of wind energy as an intermittent renewable resource with additional non-stationary nature. In this context, we analyze the power time series measured in an offshore wind farm for a total period of one year with a time resolution of 10 min. Applying detrended fluctuation analysis, we characterize the autocorrelation of power time series and find a Hurst exponent in the persistent regime with cross-over behavior. To enrich the modeling perspective of complex large wind energy systems, we develop a stochastic reduced-form model ofpower time series. The observed transitions between two dominating power generation phases are reflected by a bistable deterministic component, while correlated stochastic fluctuations account for the identified persistence. The model succeeds to qualitatively reproduce several empirical characteristics such as the autocorrelation function and the bimodal probability density function., Comment: 20 pages, 8 figures

Recent advancements in detrended fluctuation analysis (DFA) allow evaluating multifractal coefficients scale-by-scale, a promising approach for assessing the complexity of biomedical signals. The multifractality degree is typically quantified by the singularity spectrum width ( W SS ), a method that is critically unstable in multiscale applications. Thus, we aim to propose a robust multiscale index of multifractality, compare it with W SS and illustrate its performance on real biosignals. The proposed index is the cumulative function of squared increments between consecutive DFA coefficients at each scale n : α CF ( n ). We compared it with W SS calculated scale-by-scale considering monofractal/monoscale, monofractal/multiscale, multifractal/monoscale and multifractal/multiscale random processes. The two indices provided qualitatively similar descriptions of multifractality, but α CF ( n ) differentiated better the multifractal components from artefacts due to crossovers or detrending overfitting. Applied on 24 h heart rate recordings of 14 participants, the singularity spectrum failed to always satisfy the concavity requirement for providing meaningful W SS , while α CF ( n ) demonstrated a statistically significant heart rate multifractality at night in the scale ranges 16–100 and 256–680 s. Furthermore, α CF ( n ) did not reject the hypothesis of monofractality at daytime, coherently with previous reports of lower nonlinearity and monoscale multifractality during the day. Thus, α CF ( n ) appears a robust index of multiscale multifractality that is useful for quantifying complexity alterations of physiological series. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.

Prognostic techniques play a critical role in predicting upcoming faults and failures in machinery or a system by monitoring any deviation in the operation. This paper presents a novel method to analyze multidimensional sensory data and use its characteristics in bearing health prognostics. Firstly, detrended fluctuation analysis (DFA) is exploited to evaluate the long-range correlations in ball bearing vibration data. The results reveal the existence of the crossover phenomenon in vibration data with two scaling exponents at the short-range and long-range scales. Among several data sets, applying the DFA method to vibration signals shows a consistent increase in the short-range scaling exponent toward bearing failure. Finally, Kendall&rsquo, s tau is used as a ranking coefficient to quantify the trend in the scaling exponent. It was found that the Kendall&rsquo, s tau coefficient of the vibration scaling exponent could provide an early warning signal (EWS) for bearing failure.

Recent developments of detrended fluctuation analysis (DFA) provide multifractal/multiscale (MFMS) descriptions of the heart rate self-similarity, a promising approach to cardiovascular complexity. However, it is unclear whether the MFMS DFA may also describe the nonlinear components of heart rate variability. Our aim is to define MFMS DFA indices for quantifying the short-term and long-term degree of the heart-rate nonlinearity and to apply these indices to detect possible sex-related differences.We recorded the inter-beat-interval (IBI) series in 42 male and in 42 female healthy participants sitting at rest for about 2 hours. For each series j, we generated 100 phase-randomized surrogate series. We applied the MFMS DFA to estimate the self-similarity coefficients α over scales τ between 8 and 512 s and moment orders q between –5 and +5, obtaining coefficients for the original series, αO,j (q, τ), and for each surrogate, αi,j (q, τ) with 1≤i≤100. We first evaluated πj(q, τ), percentile of αi,j (q, τ) distribution in which was αO,j (q, τ). Then we calculated the percentages of scales where πj(q, τ) was 1 (q)) and for 16≤τ≤512 s (long-term nonlinearity index NL 2 (q)). We found that NL 1 (q) was generally greater than 50% at all q≥0 but q=2 (i.e., moment order of the monofractal DFA), while at q

Some physiological series, like the cardiovascular signals, show multifractal structures that depend on the temporal scale. Thus, their fractal nature is better assessed by a Detrended Fluctuation Analysis (DFA) approach that provides multifractal coefficients scale by scale. Our aim is to compare two estimators of the degree of scale-by-scale multifractality based on the width of the singularity spectrum or on the statistical dispersion of the DFA coefficients. We synthesized 1000 series of white noise (monofractal and monoscale), of autoregressive noise (monofractal and multiscale) and of Cauchy-distributed noise (multifractal and monoscale) comparing the two estimators at scales between 8 and 228 samples. We found that the two estimators provide similar scale-by-scale profiles of multifractality. However, the statistical-dispersion estimator better distinguishes multifractal from monofractal noises at all the scales, thus appearing more suitable than the singularity-spectrum width to describe the fractal structure of physiological time series.

This article is devoted to the comparative analysis of four statistical methods: Variance-time, Rescaled Range (R/S), Periodogram and Detrended Fluctuation Analysis (DFA) to study the fractal time series that are simulated by applying an algorithm, based on the Fractional Gaussian Noise (FGN). The evaluation of the statistical methods studied was performed with respect to the precision parameter in determining the Hurst exponents of the simulated fractal time series, with DFA being determined as the most accurate method. The statistical methods studied are characterized by universal against the data examined. In the article, the DFA method is applied to the analysis of two independent types of real-time data representing continuous time series: in the field of cardiology in the study of heart intervals (RR time series) obtained from digital electrocardiographic (ECG) signals and in the field of the energy for the analysis of time series data for the hourly day-ahead spot prices of the Bulgarian electricity market for the period 2016-2019. The results of the application of fractal analysis in modern information technologies related to fractal theory presented in the article reveal great potential and new perspectives in modelling, processing and analysis of processes and signals in various scientific and technical fields.

Detrended fluctuation analysis (DFA) is one of the most widely used tools for the detection of long-range dependence in time series. Although DFA has found many interesting applications and has been shown to be one of the best performing detrending methods, its probabilistic foundations are still unclear. In this paper, we study probabilistic properties of DFA for Gaussian processes. Our main attention is paid to the distribution of the squared error sum of the detrended process. We use a probabilistic approach to derive general formulas for the expected value and the variance of the squared fluctuation function of DFA for Gaussian processes. We also get analytical results for the expected value of the squared fluctuation function for particular examples of Gaussian processes, such as Gaussian white noise, fractional Gaussian noise, ordinary Brownian motion, and fractional Brownian motion. Our analytical formulas are supported by numerical simulations. The results obtained can serve as a starting point for analyzing the statistical properties of DFA-based estimators for the fluctuation function and long-memory parameter.

The heart-rate dynamics are one of the most analyzed physiological interactions. Many mathematical methods were proposed to evaluate heart-rate variability. These methods have been successfully applied in research to expand knowledge concerning the cardiovascular dynamics in healthy as well as in pathological conditions. Notwithstanding, they are still far from clinical practice. In this paper, we aim to review the nonlinear methods most used to assess heart-rate dynamics. We focused on methods based on concepts of chaos, fractality, and complexity: Poincaré plot, recurrence plot analysis, fractal dimension (and the correlation dimension), detrended fluctuation analysis, Hurst exponent, Lyapunov exponent entropies (Shannon, conditional, approximate, sample entropy, and multiscale entropy), and symbolic dynamics. We present the description of the methods along with their most notable applications.

Frequency measurements indicate the state of a power grid. In fact, deviations from the nominal frequency determine whether the grid is stable or in a critical situation. We aim to understand the fluctuations of the frequency on multiple time scales with a recently proposed method based on detrended fluctuation analysis. It enables us to infer characteristic time scales and generate stochastic models. We capture and quantify known features of the fluctuations like periodicity due to the trading market, response to variations by control systems, and stability of the long time average. We discuss similarities and differences between the British grid and the continental European grid.

We have investigated dynamics of the Internet performance through the assessment of scaling features of a network ICMP echo mechanism or pinging. Time series of round-trip times (RTT) from the host computer to 5 destination hosts and back, recorded during three consecutive days and nights, have been used. To assess correlation and scaling features of network echo mechanism, we used method of detrended fluctuation analysis (DFA) for RTT data sets. It was shown that for different, 10 minute long periods of day and night observations, RTT data sets mostly fluctuate within a narrow range, though sometimes we observe strong sharp spikes. RTT variations mostly reveal persistent behavior. DFA fluctuation curves often are characterized by crossovers indication stronger or lesser changes in the dynamics of network performance. Distribution function of DFA scaling exponents of considered RTT time series mostly was asymmetric with long tail on the right hand side. Dynamical changes occurring in the scaling features of Internet network as assessed by RTT fluctuations do not depend on the location of the host and destination nodes. Larger delays in round-trip time responses make the scaling behavior of the RTT series complicated and strongly influence their long range correlation features.

In this paper, we mainly study the local irreversibility of time series. For this purpose, inspired by the idea of segmentation contained in multi-fractal detrended fluctuation analysis, we propose a novel time series irreversibility analysis, that is, multi-scale local irreversibility method based on the segmentation. By means of this method, we can availably discuss the fluctuation of local irreversibility of a time series as the timescale changes. In order to explore the performance of this method in analyzing the local irreversibility of time series in depth, we provide the definition of Kullback–Leibler distribution (KLD) combined with the horizontal visibility graph algorithm, which has been verified to be an intuitive method for the analysis of irreversibility. Meanwhile, we also introduce three other irreversible indices: Porta’s, Guzik’s and Ehler’s indices ($$P\%$$, $$G\%$$ and E, respectively) for deeper detection. Further on, we first apply the above methods to the simulated time series generated by the auto-regressive fractionally integrated moving average model and financial time series. By virtue of the new multi-scale local irreversibility analysis method, the results show that KLD and Ehler’s index are superior to the other two measures when comparing the difference of local irreversibility of different time series. At the same time, we also find an interesting phenomenon, that is, results of Porta’s index are similar to those of Guzik’s index and both of them can better expose the similarity of local irreversibility of different time series. Besides, all of them can exhibit their powerful function in analyzing the relationship of the holistic and local irreversibility of a time series. As a further validation, we specially examine effect of the new method on the analysis to different electrocardiogram signals. The results provide us with plenty of meaningful results, which are consequential for disease detection.

We study the scaling features in the evolutionary dynamics of two coupled chaotic systems based on the sequences of return times into a Poincare section, contaminated with additive (measuring) noise. Using three models of chaotic systems: the Rossler oscillator, the Lorenz system, and the nephron model, and the detrended fluctuation analysis (DFA) as an approach for data processing, we demonstrate that the anti-correlated sequences of return times of synchronous motions show a higher sensitivity to measuring noise than the positively correlated series of return times of asynchronous oscillations. This conclusion is confirmed by the results for various oscillatory regimes in all models considered.

The purpose of this study was to explore the extent of multifractality in unperturbed and constrained locomotion, and to determine if multifractality predicted gait adaptability. Young, healthy participants (n = 15) walked at preferred and slow speeds, as well as asymmetrically (one leg at half speed) on a split-belt treadmill. Stride time multifractality was assessed via local detrended fluctuation analysis, which evaluates the evolution of fluctuations both spatially and temporally. Unperturbed walking exhibited monofractal behavior. Asymmetric walking displayed greater multifractality in the faster moving limb, indicating more intermittent periods of extreme high or low variance. Multifractality was not associated with adaptation to asymmetric walking. These findings further suggest that unperturbed locomotion is monofractal and establish that perturbed walking yields multifractal behavior.

Detrended fluctuation analysis (DFA) is a standard method to evaluate long-range correlations embedded in non-stationary time series. To obtain a reliable estimation of scaling behavior, it requires the length of a time series is long enough (at least ∼ 10 , 000 ), which is not always the case in reality. How to evaluate long-range correlation behavior in a very short time series is still an open problem. In the present paper, we propose an improvement of DFA by correcting the bias in estimation of variance, called Unbiased Detrended Fluctuation Analysis (UDFA). Extensive calculations show its high-performance. For instance, from a fractional Brownian motion (fBm) series with length 500 the estimated long-range correlation exponent has negligible bias and acceptable confidence region (standard deviation less than 0.05). As a typical example, the proposed method is used to monitor evolution of fractal gait rhythm of a volunteer. Rich patterns are found in the evolutionary process.

In the recent past, coronal mass ejection (CME) has received much research attention for its geo-effectiveness. In this paper, an investigation has been made to identify the scaling pattern of the CME linear speed time series data (February 1999 to December 2007 of solar cycle 23) collected from the Solar and Heliospheric Observatory (SOHO) using Multi-Fractal Detrended Fluctuation Analysis (MFDFA) and Multi-Fractal Detrended Moving Average (MFDMA) method. The scaling exponent, generalized Hurst exponent, singularity strength and also the singularity spectrum have been computed to quantify the multifractality and to identify the singularities of the time series data. An effort has also been made to find out the possible sources which are responsible for the multifractality in the signal by studying the scaling patterns of the shuffled and surrogate version of the original data. It has been revealed in this paper that CME linear speed signal exhibit multifractal behaviour with long-term persistence. Both the long-range temporal correlation and the broad probability density function (pdf) are found to be the primary source of multifractality in the signal. The singularities or abruptness present in the signal are found to vary with time, and this fluctuation follows an AR (2) model.

The purpose of this paper is to propose a new estimator of Hurst exponent based on the combined information of the conventional rescaled range methods. We demonstrate the superiority of the proposed estimator by Monte Carlo simulations, and the applications in estimating the Hurst exponent of daily volatility series in Chinese stock market. Moreover, we indicate the impact of the type of estimator and structural break on the estimating results of Hurst exponent.

Long-range temporal dependence is an important research perspective for modelling of traffic flow time series. Various methods have been proposed to depict the long-range temporal dependence, including autocorrelation function analysis, spectral analysis and fractal analysis. However, few researches have studied the daily temporal dependence (i.e. the similarity between different daily traffic flow time series), which can help us better understand the long-range temporal dependence, such as the origin of crossover phenomenon. Moreover, considering both types of dependence contributes to establishing more accurate model and depicting the properties of traffic flow time series. In this paper, we study the properties of daily temporal dependence by simple average method and Principal Component Analysis (PCA) based method. Meanwhile, we also study the long-range temporal dependence by Detrended Fluctuation Analysis (DFA) and Multifractal Detrended Fluctuation Analysis (MFDFA). The results show that both the daily and long-range temporal dependence exert considerable influence on the traffic flow series. The DFA results reveal that the daily temporal dependence creates crossover phenomenon when estimating the Hurst exponent which depicts the long-range temporal dependence. Furthermore, through the comparison of the DFA test, PCA-based method turns out to be a better method to extract the daily temporal dependence especially when the difference between days is significant.

Many natural and physical processes display long memory and extreme events. In these systems, the measured time series is invariably contaminated by noise. As the extreme events display large deviation from the mean behaviour, the noise does not affect the extreme events as much as it affects the typical values. Since the extreme events also carry the information about correlations in the full time series, they can be used to infer the correlation properties of the latter. In this work, from a given time series, we construct three modified time series using only the extreme events. It is shown that the correlations in the original time series and in the modified time series, as measured by the exponent obtained from detrended fluctuation analysis technique, are related to each other. Hence, the correlation exponents for a long memory time series can be inferred from its extreme events alone. This approach is demonstrated for several empirical time series., 9 pages, 7 figures

A new methodology is proposed here to identify anomalies in the neutron flux as a result neutron production trips in an Accelerator Driven System - ADS type reactor. This methodology is based on the calculation of Hurst exponents, where the neutron flux as monitored in the reactor core is treated as a temporal series. In several recent articles, related to fractional diffusion, the Hurst exponent is used as an estimate for the order of the fractional derivative. Our object of study considered a reactor based on the Myrrha simulated with the Serpent Monte Carlo code for two kinds of trips occurred in the production of neutrons, as follows: a Peak of Production (PP), the Unprotected* Accelerator Beam Overpower (UABO), and the spurious Beam Trip (BT). In order to estimate the Hurst exponent it was used two different methods, namely the Rescaled Range Analysis (R/S) and the Detrended Fluctuation Analysis Method (DFA). The results obtained showed that the R/S methodology had some advantages in a comparison with the DFA in indicating the occurrence of those anomalies. Also being able to provide a scale for the assessment of the intensity of the trip occurred, showing to be an useful tool to support anomaly identification in the neutron flux of ADS reactors.

In the present paper, for a symmetrical bistable system that is excited by a fractional Gaussian noise, via the examination upon the qualitative changes of the stationary probability densities, the phenomena of the noise induced transition and escape and the stochastic resonance, which is in the sense of that the particles oscillate between the double-well potential, are investigated. For a high noise intensity, the probability density function obtained by Monte Carlo method changes from bimodal to unimodal by decreasing the values of Hurst index H. However, in the low noise intensity regime the transition could not occur for all H. Based on numerical results we demonstrate the fact that the mean first passage time (MFPT) is dependent on the Hurst index H and the noise intensity D, and possesses an exponential form as T ( D , H ) = k 1 ( H ) exp ⁡ ( k 2 ( H ) / D ) . In particular, with a higher noise intensity, T ( D , H ) represents itself as a monotonous increasing function of the MFPT with the increasing H, whereas it exhibits as a non-monotonic function of H in the low noise intensity regime. Finally, nonlinear response theory is applied to investigate the stochastic resonance induced by Hurst index H and noise intensity D, for which we find that only for low noise intensities the stochastic resonance exists via increasing Hurst index which means the effect of noise reduction. This phenomenon is quite different from the one of the classical case.

We investigate the self-affinity of a fluid velocity time series in turbulent regime. We use the CFD OpenFoam library to simulate the flow of fluid in a duct with two obstacles injecting vorticity downstream. We calculated the fluid velocity in three sections in of the structure. The simulation presented more turbulences in sections further away from obstacles. We assess the self-affinity of properties of those sections by using the Detrended Fluctuation Analysis method. This scaling method presented two behaviors. First, a persistent behavior after passing through obstacles. Second, a sub-diffusive behavior in turbulent sections, presenting a long-range correlation α exponent ranging between 1.0 and 1.5. The findings suggest that chaotic states tend to follow a sub-diffusive process.

There are a number of situations in which several signals are simultaneously recorded in complex systems, which exhibit long-term power-law cross-correlations. This paper presents two-dimensional multifractal cross-correlation analysis based on the partition function (2D-MFXPF), two-dimensional multifractal cross-correlation analysis based on the detrended fluctuation analysis (2D-MFXDFA) and two-dimensional multifractal cross-correlation analysis based on the detrended moving average analysis (2D-MFXDMA). We apply these methods to pairs of two-dimensional multiplicative cascades (2D-MC) to do a comparative study. Then, we apply the two-dimensional multifractal cross-correlation analysis based on the detrended fluctuation analysis (2D-MFXDFA) to real images and unveil intriguing multifractality in the cross correlations of the material structures. At last, we give the main conclusions and provide a valuable reference on how to choose the multifractal algorithms in the potential applications in the field of SAR image classification and detection.

Interaction patterns among different warehouses could make the warehouse-out behavioral sequences less predictable. We firstly take a coupling detrended fluctuation analysis on the warehouse-out quantity, and find that the multivariate sequences exhibit significant coupling multifractal characteristics regardless of the types of steel products. Secondly, we track the sources of multifractal warehouse-out sequences by shuffling and surrogating original ones, and we find that fat-tail distribution contributes more to multifractal features than the long-term memory, regardless of types of steel products. From perspective of warehouse contribution, some warehouses steadily contribute more to multifractal than other warehouses. Finally, based on multiscale multifractal analysis, we propose Hurst surface structure to investigate coupling multifractal, and show that multiple behavioral sequences exhibit significant coupling multifractal features that emerge and usually be restricted within relatively greater time scale interval.

Numerous investigations have been performed to establish a correlation between earthquakes and the geological parameters. Radon is one of the general criterions that is referred for the examination of this correlation. Chaotic signal processing methods has a very important explain of complex dynamical system for example earthquakes. Scale analyses technique, Detrended Fluctuation Analysis (DFA), has similar exponents with RMS (Root Mean Square). Hurst exponent is an enormous method to prediction long-range power law. Multifractal time series have both excessively great and little ripple. Made in this study, Detrended Fluctuation Analysis was performed for 222Rn data that was gathered from Lesvos Island in Greece. The results of Detrended Fluctuation Analysis have a robust correlation for each 222Rn time series.

Detrended fluctuation analysis (DFA) is a popular method to analyze long-range temporal correlations in time series of many different research areas but in particular also for electrophysiological recordings. Using the classical DFA method, the cumulative sum of data are divided into segments, and the variance of these sums is studied as a function of segment length after linearly detrending them in each segment. The starting point of the proposed new method is the observation that the classical method is inherently non-stationary without justification by a corresponding non-stationarity of the data. This leads to unstable estimates of fluctuations to the extent that it is impossible to estimate slopes of the fluctuations other than by fitting a line over a wide range of temporal scales. We here use a modification of the classical method by formulating the detrending as a strictly stationary operation. With this modification the detrended fluctuations can be expressed as a weighted average across the power spectrum of a signal. Most importantly, we can also express the slopes, calculated as analytic derivatives of the fluctuations with respect to the scales, as statistically robust weighted averages across the power spectra. The method is applied to amplitudes of brain oscillations measured with magnetoencephalography in resting state condition. We found for envelopes of the the alpha rhythm that fluctuations as a function of time scales in a double-logarithmic plot differ substantially from a linear relation for time scales below 10 seconds. In particular we will show that model selections fail to determine accurate scaling laws, and that standard parameter settings are likely to yield results depending on signal to noise ratios than on true long range temporal correlations.

This chapter deals with a novel methodology for the analysis of fractal time series and point processes known as Natural Time Analysis. It was introduced in 2001 by a group of Greek scientists leaded by P. Varotsos. Currently natural time analysis is being widely applied to the study of signals originated from complex systems. The method has also been applied successfully to the analysis of data sets of seismic processes from many regions of the world. In particular, natural time analysis has been found to be consistent with the methods of detrended fluctuation analysis and multifractal characterization, among others.

Fractal processes have recently received a growing interest, especially in the domain of rehabilitation. More precisely, the evolution of fractality with aging and disease, suggesting a loss of complexity, has inspired a number of studies that tried, for example, to entrain patients with fractal rhythms. This kind of study requires relevant methods for generating fractal signals and for assessing the fractality of the series produced by participants. In the present work, we engaged a cross validation of three methods of generation and three methods of analysis. We generated exact fractal series with the Davies–Harte (DH) algorithm, the spectral synthesis method (SSM), and the ARFIMA simulation method. The series were analyzed by detrended fluctuation analysis (DFA), power spectral density (PSD) method, and ARFIMA modeling. Results show that some methods of generation present systematic biases: DH presented a strong bias toward white noise in fBm series close to the 1/f boundary and SSM produced series with a larger variability around the expected exponent, as compared with other methods. In contrast, ARFIMA simulations provided quite accurate series, without major bias. Concerning the methods of analysis, DFA tended to systematically underestimate fBm series. In contrast, PSD yielded overestimates for fBm series. With DFA, the variability of estimates tended to increase for fGn series as they approached the 1/f boundary and reached unacceptable levels for fBm series. The highest levels of variability were produced by PSD. Finally, ARFIMA methods generated the best series and provided the most accurate and less variable estimates.

In this work we study different characteristics of fog long-term persistence, in events with different physical formation mechanisms. Specifically, we focus on the characterization of fog long-term persistence from observational data, by means of a Detrended Fluctuation Analysis (DFA) of its associated low-visibility time series. We analyze fog events with radiation and orographic underlying physical formation mechanisms, and identify a two-range pattern of long-term persistence. Our analysis leads to the emergence of a characteristic time, τ∗, at the crossover point between different scaling exponents in the DFA, independent of the time scale at which the fog event is studied. We also show that these scale-invariant characteristic times are related to the duration of fog/no-fog periods, what suggests that τ∗ is a good measure to study and compare fog events persistence, and also that fog events can be studied as on-off intermittent processes. We apply a binarizing process and confirm that the two-range shape structure in the DFA is maintained in the binary time series associated to fog events. We also show how τ∗ is related to the binary structure of the time series.

Multifractal Detrended Fluctuation Analysis (MFDFA) is an efficient method to investigate the long-term correlations of the power law of non-stationary time series, in which the elimination of local trends usually depends upon a fixed-constant polynomial order. In this paper, we propose a flexible set of polynomial and trigonometric functions to better detect, and correctly model, hidden local trends in the time series at different scales. We introduce the Multifractal Detrended Fluctuation Analysis with Polynomial and Trigonometric functions (MFDFAPT) method via optimal model selection from an optimization framework. The performance of MFDFAPT is assessed with extensive numerical experiments based on the multifractal binomial cascade process, fractional Brownian movements, and fractional Gaussian noises. MFDFAPT shows better performance than MFDFA in the approximation of the Hurst index, and correctly determines the scalar behavior in stationary and non-stationary series. Additionally we apply MFDFAPT to detect and characterize the scalar properties of the daily precipitation time series in meteorological stations of Tabasco, Mexico. Our results confirm previous indications that the general Hurst exponent depends on the physiographic characteristics of the study area, and that fractal dimension correctly characterizes the series of daily precipitations in tropical regions.

High frequency data from the city of Petrolina, Northeast Brazil, are used to find the wind speed projection series at different angles, which are then analyzed by using several statistical tools: detrended fluctuation analysis (DFA), multifractal detrended fluctuation analysis (MFDFA) and Fisher–Shannon analysis (FSA). It is found that α , the DFA scaling exponent, varies between 0.6 (indicating weak persistence) and unity (strong persistence), at different time frames and at different projection angles. At lower projection angles wind speed is characterized by higher heterogeneity, lower disorder, higher organization and relative dominance of small fluctuations. The statistical analysis of wind speed with varying the direction could contribute to enrich the knowledge of the dynamics of wind speed.