detrended fluctuation analysis based on higher order moments of financial time series in this paper a generalized method of detrended fluctuation analysis dfa is proposed as a new measure to assess the complexity of a complex dynamical system such as stock market we extend dfa and local scaling dfa to higher moments such as skewness and kurtosis labeled smdfa and kmdfa so as to investigate the volatility scaling property of financial time series simulations are conducted over synthetic and financial data for providing the comparative study we further report the results of volatility behaviors in three american countries three chinese and three european stock markets by using dfa and lsdfa method based on higher moments they demonstrate the dynamics behaviors of time series in different aspects which can quantify the changes of complexity for stock market data and provide us with more meaningful information than single exponent and the results reveal some higher moments volatility and higher moments multiscale volatility details that cannot be obtained using the traditional dfa method

multifractal properties of the indian financial market abstract we investigate the multifractal properties of the logarithmic returns of the indian financial indices bse nse by applying the multifractal detrended fluctuation analysis the results are compared with that of the us s p 500 index numerically we find that q th order generalized hurst exponents h q and q change with the moments q the nonlinear dependence of these scaling exponents and the singularity spectrum f show that the returns possess multifractality by comparing the mf dfa results of the original series to those for the shuffled series we find that the multifractality is due to the contributions of long range correlations as well as the broad probability density function the financial markets studied here are compared with the binomial multifractal model bmfm and have a smaller multifractal strength than the bmfm

statistical properties of german dax and chinese indices abstract we investigate statistical properties of the german dax and chinese indices including the volatility distribution autocorrelation function dfa function and return volatility correlation function with both the daily data and minutely data at the minutely time scale the chinese indices may show irregular dynamic behavior at the daily time scale the volatility distribution autocorrelation function and dfa function of the chinese indices are qualitatively similar to those of the german dax while the return volatility correlation function exhibits an anti leverage effect different from the leverage effect of the german dax

multifractal property of chinese stock market in the csi 800 index based on mf dfa approach abstract csi 800 index consists of csi 500 index and csi 300 index aiming to reflect the performance of stocks with large mid and small size of china a share market in this paper we analyze the multifractal structure of chinese stock market in the csi 800 index based on the multifractal detrended fluctuation analysis mf dfa method we find that the fluctuation of the closing logarithmic returns have multifractal properties the shape and width of multifractal spectrum are depended on the weighing order q more interestingly we observe a bigger market crash in june august 2015 than the one in 2008 based on the local hurst exponents the result provides important information for further study on dynamic mechanism of return fluctuation and whether it would trigger a new financial crisis

statistical properties of daily ensemble variables in the chinese stock markets abstract we study dynamical behavior of the chinese stock markets by investigating the statistical properties of daily ensemble return and variety defined respectively as the mean and the standard deviation of the ensemble daily price return of a portfolio of stocks traded in china s stock markets on a given day the distribution of the daily ensemble return has an exponential form in the center and power law tails while the variety distribution is lognormal in the bulk followed by a power law tail for large variety based on detrended fluctuation analysis r s analysis and modified r s analysis we find evidence of long memory in the ensemble return and strong evidence of long memory in the evolution of variety

doubly lexical orderings of matrices every matrix has a doubly lexical ordering an ordering of the rows and columns so that the row vectors are lexically or lexicographically increasing and the column vectors are lexically increasing every graph has a lexical ordering a vertex ordering making the neighbourhood matrix doubly lexical an almost linear time doubly lexical ordering algorithm is given doubly lexical orderings unify the orderings characterizing certain classes of matrices and graphs including totally balanced matrices subtree matrices and chordal graphs

state feedback control of non linear systems a design method for state feedback controllers for single input non linear systems is proposed the method makes use of the transformations of the non linear system into controllable like canonical forms the resulting non linear state feedback is designed in such a way that the eigenvalues of the linearized closed loop model are invariant with respect to any constant operating point the method constitutes an alternative approach to the design methodology recently proposed by baumann and rugh also a review of different transformation methods for non linear systems is presented an example and simulation results of different control strategies are provided to illustrate the design technique

nonlinear control via uniform system approximation abstract we introduce the notion of uniform system approximation and use it to solve the following problem given a single input nonlinear system f g x f x g x u that is linearly controllable on a manifold e of equilibrium points find vector fields f g such that f g and f g agree to first order on e and f g is input to state linearizable on a neighborhood of e the solution proceeds by constructing a linearizable uniform system approximation and yields as a by product a feedback control law that can be used to guarantee stable operation of the given system in a neighborhood of e this includes for example smooth transitions between operating points that are not necessarily close to each other a simple example is given to illustrate the approach

detrending moving average algorithm for multifractals the detrending moving average dma algorithm is a widely used technique to quantify the long term correlations of nonstationary time series and the long range correlations of fractal surfaces which contains a parameter ensuremath theta determining the position of the detrending window we develop multifractal detrending moving average mfdma algorithms for the analysis of one dimensional multifractal measures and higher dimensional multifractals which is a generalization of the dma method the performance of the one dimensional and two dimensional mfdma methods is investigated using synthetic multifractal measures with analytical solutions for backward ensuremath theta 0 centered ensuremath theta 0 5 and forward ensuremath theta 1 detrending windows we find that the estimated multifractal scaling exponent ensuremath tau q and the singularity spectrum f ensuremath alpha are in good agreement with the theoretical values in addition the backward mfdma method has the best performance which provides the most accurate estimates of the scaling exponents with lowest error bars while the centered mfdma method has the worse performance it is found that the backward mfdma algorithm also outperforms the multifractal detrended fluctuation analysis the one dimensional backward mfdma method is applied to analyzing the time series of shanghai stock exchange composite index and its multifractal nature is confirmed

a threshold of ln n for approximating set cover given a collection f of subsets of s 1 n setcover is the problem of selecting as few as possiblesubsets from f such that their union covers s and maxk cover is the problem of selecting k subsets from f such that their union has maximum cardinality both these problems arenp hard we prove that 1 o 1 ln n is a threshold below which setcover cannot be approximated efficiently unless np has slightlysuperpolynomial time algorithms this closes the gap up to low orderterms between the ratio of approximation achievable by the greedyalogorithm which is 1 o 1 lnn and provious results of lund and yanakakis that showed hardness ofapproximation within a ratio of log 2 n 2s0 72 ln n for max k cover we show an approximationthreshold of 1 1 e up tolow order terms under assumption that p np

comparison of detrending methods for fluctuation analysis abstract we examine several recently suggested methods for the detection of long range correlations in data series based on similar ideas as the well established detrended fluctuation analysis dfa in particular we present a detailed comparison between the regular dfa and two recently suggested methods the centered moving average cma method and a modified detrended fluctuation analysis mdfa we find that cma performs the same as dfa in long data with weak trends and is slightly superior to dfa in short data with weak trends when comparing standard dfa to mdfa we observe that dfa performs slightly better in almost all examples we studied we also discuss how several types of trends affect different types of dfa for weak trends in the data the new methods are comparable with dfa in these respects however if the functional form of the trend in data is not a priori known dfa remains the method of choice only a comparison of dfa results using different detrending polynomials yields full recognition of the trends a comparison with independent methods is recommended for proving long range correlations

technical communique input output pseudolinearization on controlled invariant submanifolds the original notion of input output pseudolinearization on equilibrium submanifolds is extended to general controlled invariant submanifolds for nonlinear systems input output pseudolinearizing feedback laws render the input output behavior of linearizations about nominal trajectories in the submanifold trajectory independent and equal to that of a fixed linear time invariant system thereby facilitating the subsequent construction of control laws for stabilization and tracking sufficient existence conditions are derived that are weaker than the familiar relative degree conditions for nonlinear input output decoupling that have come to be associated with exact input output linearization

multifractal detrended fluctuation analysis of nonstationary time series abstract we develop a method for the multifractal characterization of nonstationary time series which is based on a generalization of the detrended fluctuation analysis dfa we relate our multifractal dfa method to the standard partition function based multifractal formalism and prove that both approaches are equivalent for stationary signals with compact support by analyzing several examples we show that the new method can reliably determine the multifractal scaling behavior of time series by comparing the multifractal dfa results for original series with those for shuffled series we can distinguish multifractality due to long range correlations from multifractality due to a broad probability density function we also compare our results with the wavelet transform modulus maxima method and show that the results are equivalent

a new method for linearizing non linear systems the pseudolinearization consider the single input non linear system x f x u it has been shown that under mild conditions there exist transformations z t x v s x u such that in the z space the linear tangent model is independent of the operating point a constructive procedure to obtain the above transformations is described this new method is computationally attractive and effective in many practical cases

financial multifractality and its subtleties an example of dax abstract detailed study of multifractal characteristics of the financial time series of asset values and of its returns is performed using a collection of the high frequency deutsche aktienindex data the tail index the renyi exponents based on the box counting algorithm for the graph d q and the generalized hurst exponents h q are computed in parallel for short and daily return times the results indicate a more complicated nature of the stock market dynamics than just consistent multifractal

research on the fractal structure in the chinese stock market abstract applying fractal theory this paper probes and discusses self similarity and scale invariance of the chinese stock market it analyses three kinds of scale indexes i e autocorrelation index hurst index and the scale index on the basis of detrended fluctuation analysis dfa algorithm and promotes dfa into a recursive algorithm using the three kinds of scale indexes we conduct empirical research on the chinese shanghai and shenzhen stock markets the results indicate that the rate of returns of the two stock markets does not obey the normal distribution a correlation exists between the stock price indexes over time scales the stock price indexes exhibit fractal time series it indicates that the policy guide hidden at the back influences the characteristic of the chinese stock market

unbiased detrended fluctuation analysis long range correlations in very short time series abstract 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

statistical properties of the volatility of price fluctuations we study the statistical properties of volatility measured by locally averaging over a time window t the absolute value of price changes over a short time interval ensuremath delta t we analyze the s 500 stock index for the 13 year period jan 1984 to dec 1996 we find that the cumulative distribution of the volatility is consistent with a power law asymptotic behavior characterized by an exponent ensuremath mu ensuremath approx 3 similar to what is found for the distribution of price changes the volatility distribution retains the same functional form for a range of values of t further we study the volatility correlations by using the power spectrum analysis both methods support a power law decay of the correlation function and give consistent estimates of the relevant scaling exponents also both methods show the presence of a crossover at approximately 1 5 days in addition we extend these results to the volatility of individual companies by analyzing a data base comprising all trades for the largest 500 u s companies over the two year period jan 1994 to dec 1995

application of multifractal measures to tehran price index we report an empirical study of tehran price index tepix to analyze our data we use various methods like as rescaled range analysis r s modified rescaled range analysis lo s method detrended fluctuation analysis dfa and generalized hurst exponents analysis based on numerical results the scaling range of tepix returns is specified long memory effect or long range correlation property in this market is investigated characteristic exponent for probability distribution function of tepix returns is derived and finally the stage of development in tehran stock exchange is determined

an extended linearization approach to nonlinear system inversion the inversion problem for nonlinear systems is considered from both the internal viewpoint state equation representations and the external viewpoint volterra series representations in order to circumvent difficulties in the known exact inversion theory a method for obtaining what will be called a partial inverse system is proposed for a given nonlinear system a cascade system typically containing differentiators is found such that the overall nonlinear system has a family of constant operating points with inputs and outputs identical furthermore the linearization of the overall system about each of these constant operating points is the identity system

assessment of 48 stock markets using adaptive multifractal approach abstract in this paper stock market comovements are examined using cointegration granger causality tests and nonlinear approaches in context of mutual information and correlations since underlying data sets are affected by non stationarities and trends we also apply adaptive multifractal detrended fluctuation analysis amf dfa and adaptive multifractal detrended cross correlation analysis amf dxa we find only 170 pair of stock markets cointegrated and according to the granger causality and mutual information we realize that the strongest relations lies between emerging markets and between emerging and frontier markets according to scaling exponent given by amf dfa h q 2 1 we find that all underlying data sets belong to non stationary process according to efficient market hypothesis emh only 8 markets are classified in uncorrelated processes at 2 confidence interval 6 stock markets belong to anti correlated class and dominant part of markets has memory in corresponding daily index prices during january 1995 to february 2014 new zealand with h 0 457 0 004 and jordan with h 0 602 0 006 are far from emh the nature of cross correlation exponents based on amf dxa is almost multifractal for all pair of stock markets the empirical relation h x y h x x h y y 2 is confirmed mentioned relation for q 0 is also satisfied while for q 0 there is a deviation from this relation confirming behavior of markets for small fluctuations is affected by contribution of major pair for larger fluctuations the cross correlation contains information from both local internal and global external conditions width of singularity spectrum for auto correlation and cross correlation are x x 0 304 0 905 and x y 0 246 1 178 respectively the wide range of singularity spectrum for cross correlation confirms that the bilateral relation between stock markets is more complex the value of d c c a indicates that all pairs of stock market studied in this time interval belong to cross correlated processes

long term memory and volatility clustering in high frequency price changes abstract we studied the long term memory in diverse stock market indices and foreign exchange rates using detrended fluctuation analysis dfa for all high frequency market data studied no significant long term memory property was detected in the return series while a strong long term memory property was found in the volatility time series the possible causes of the long term memory property were investigated using the return data filtered by the ar 1 model reflecting the short term memory property the garch 1 1 model reflecting the volatility clustering property and the figarch model reflecting the long term memory property of the volatility time series the memory effect in the ar 1 filtered return and volatility time series remained unchanged while the long term memory property diminished significantly in the volatility series of the garch 1 1 filtered data notably there is no long term memory property when we eliminate the long term memory property of volatility by the figarch model for all data used although the hurst exponents of the volatility time series changed considerably over time those of the time series with the volatility clustering effect removed diminish significantly our results imply that the long term memory property of the volatility time series can be attributed to the volatility clustering observed in the financial time series

power law and multiscaling properties of the chinese stock market abstract we investigate the cumulative probability density function pdf and the multiscaling properties of the returns in the chinese stock market by using returns data adjusted for thin trading we find that the distribution has power law tails at shorter microscopic timescales or lags however the distribution follows an exponential law for longer timescales furthermore we investigate the long range correlation and multifractality of the returns in the chinese stock market by the dfa and mfdfa methods we find that all the scaling exponents are between 0 5 and 1 by dfa method which exhibits the long range power law correlations in the chinese stock market moreover we find by mfdfa method that the generalized hurst exponents h q are not constants which shows the multifractality in the chinese stock market we also find that the correlation of shenzhen stock market is stronger than that of shanghai stock market

discrete scale invariance in cross correlations between time series abstract the de trended cross correlation analysis dcca is converted to a new form which turns out to be a periodic function modulated power law to evaluate discrete scale long range cross correlation between time series if the modulator is dominated with one frequency the derived form will degenerate to a log periodic power law we investigate a total of five important stock markets distributing in different continents calculations show that the cross correlations between different stock markets may hint at log periodic oscillations this finding may be helpful for us to evaluate financial state in a global way

direct determination approach for the multifractal detrending moving average analysis in the canonical framework we propose an alternative approach for the multifractal analysis based on the detrending moving average method mf dma we define a canonical measure such that the multifractal mass exponent q is related to the partition function and the multifractal spectrum f can be directly determined the performances of the direct determination approach and the traditional approach of the mf dma are compared based on three synthetic multifractal and monofractal measures generated from the one dimensional p model the two dimensional p model and the fractional brownian motions we find that both approaches have comparable performances to unveil the fractal and multifractal nature in other words without loss of accuracy the multifractal spectrum f can be directly determined using the new approach with less computation cost we also apply the new mf dma approach to the volatility time series of stock prices and confirm the presence of multifractality

pseudolinearization and nonlinear optimal control for a class of infinite horizon nonlinear optimal control problems the pseudo linearization transformation is used to investigate a new type of approximate solution for the hamilton jacobi equation and corresponding approximate optimal control law

effect of trends on detrended fluctuation analysis scaling behavior we find that crossovers result from the competition between the scaling of the noise and the apparent scaling of the trend we study how the characteristics of these crossovers depend on i the slope of the linear trend ii the amplitude and period of the periodic trend iii the amplitude and power of the power law trend and iv the length as well as the correlation properties of the noise surprisingly we find that the crossovers in the scaling of noisy signals with trends also follow scaling laws i e long range power law dependence of the position of the crossover on the parameters of the trends we show that the dfa result of noise with a trend can be exactly determined by the superposition of the separate results of the dfa on the noise and on the trend assuming that the noise and the trend are not correlated if this superposition rule is not followed this is an indication that the noise and the superposed trend are not independent so that removing the trend could lead to changes in the correlation properties of the noise in addition we show how to use dfa appropriately to minimize the effects of trends how to recognize if a crossover indicates indeed a transition from one type to a different type of underlying correlation or if the crossover is due to a trend without any transition in the dynamical properties of the noise

detrended fluctuation analysis for fractals and multifractals in higher dimensions one dimensional detrended fluctuation analysis dfa and multifractal detrended fluctuation analysis mfdfa are widely used in the scaling analysis of fractal and multifractal time series because they are accurate and easy to implement in this paper we generalize the one dimensional dfa and mfdfa to higher dimensional versions the generalization works well when tested with synthetic surfaces including fractional brownian surfaces and multifractal surfaces the two dimensional mfdfa is also adopted to analyze two images from nature and experiment and nice scaling laws are unraveled

econophysics financial time series from a statistical physics point of view in recent years physicists have started applying concepts and methods of statistical physics to study economic problems the word econophysics is sometimes used to refer to this work much recent work is focused on understanding the statistical properties of nancial time series one reason for this interest is that nancial markets are examples of complex interacting systems for which a huge amount of data exist and it is possible that nancial time series viewed from a dierent perspective might yield new results this article reviews the results of three recent phenomenological studies i the probability distribution of stock price uctuations stock price uctuations occur in all magnitudes in analogy to earthquakes from tiny uctuations to drastic events such as market crashes the distribution of price uctuations decays with a power law tail well outside the levy stable regime and describes uctuations that dier by as much as eight orders of magnitude in addition this distribution preserves its functional form for uctuations on time scales that dier by three orders of magnitude from 1 min up to approximately 10 d ii correlations in nancial time series while price uctuations themselves have rapidly decaying correlations the magnitude of uctuations measured by either the absolute value or the square of the price uctuations has correlations that decay as a power law and persist for several months iii correlations among dierent companies the third result bears on the application of random matrix theory to understand the correlations among price uctuations of any two dierent stocks from a study of the eigenvalue statistics of the cross correlation matrix constructed from price uctuations of the leading 1000 stocks we nd that the largest 1 of the eigenvalues and the corresponding eigenvectors show systematic deviations from the predictions for a random matrix whereas the rest of the eigenvalues conform to random matrix behavior suggesting that these 1 of the eigenvalues contain system specic information about correlated time evolution of dierent companies c 2000 published by elsevier science b v all rights reserved

three partition refinement algorithms we present improved partition refinement algorithms for three problems lexicographic sorting relational coarsest partition and double lexical ordering our double lexical ordering algorithm uses a new efficient method for unmerging two sorted sets

hardness results and approximation algorithms for weighted paired domination in graphs let g v e be a simple graph without isolated vertices a vertex set s v is a paired dominating set if every vertex in v s has a neighbor in s and the induced subgraph g s has a perfect matching in this paper we investigate the approximation hardness of paired domination in graphs for weighted paired domination an approximation algorithm in general graphs and an exact dynamic programming style algorithm in trees are also given

distance paired domination problems on subclasses of chordal graphs let g v e be a graph without isolated vertices for a positive integer k a set s v is a k distance paired dominating set if each vertex in v s is within distance k of a vertex in s and the subgraph induced by s contains a perfect matching in this paper we present two linear time algorithms to find a minimum cardinality k distance paired dominating set in interval graphs and block graphs which are two subclasses of chordal graphs in addition we present a characterization of trees with unique minimum k distance paired dominating set

on the linear equivalents of nonlinear systems this paper studies feedback equivalence of nonlinear systems an equivalence relation is established for the systems x f x u a complete characterization of the class of nonlinear systems which are equivalent to controllable linear systems is obtained

long range correlation and predictability of chinese stock prices abstract it is well known that the price time series of an efficient market display a brownian motion in such a case the best prediction of a future price is the last known price thus no arbitrage is possible a real market is however possibly not perfectly efficient in this paper we apply an error back propagation neural network to tick by tick high frequency time series of chinese a share stock prices and try to predict their future values the performance of the predictions varies largely for different stocks in order to seek for the dependence of the performance we study the long range correlation lrc of the data by the conventional detrended fluctuation analysis dfa and its generalization the multifractal detrended fluctuation analysis mfdfa clear relevance between the performance and the lrc is observed the prediction is particularly good when the time series are strongly anti persistent

scaling of the distribution of fluctuations of financial market indices up to more than one month we find that the distributions for dt 4d 1560 min are consistent with a power law asymptotic behavior characterized by an exponent a 3 well outside the stable levy regime 0 a 2 to test the robustness of the s p result we perform a parallel analysis on two other financial market indices database iv contains 3560 daily records of the nikkei index for the 14 year period 1984 1997 and database v contains 4649 daily records of the hang seng index for the 18 year period 1980 1997 we find estimates ofa consistent with those describing the distribution of s p 500 daily returns one possible reason for the scaling of these distributions is the long persistence of the autocorrelation function of the volatility for time scales longer than dt 3 4 d our results are consistent with a slow convergence to gaussian behavior s1063 651x 99 11211 x pacs number s 05 40 fb 05 45 tp 89 90 1n

algorithmic aspects of upper paired domination in graphs abstract a set d of vertices in a graph g is a paired dominating set of g if every vertex of g is adjacent to a vertex in d and the subgraph induced by d contains a perfect matching not necessarily as an induced subgraph a paired dominating set of g is minimal if no proper subset of it is a paired dominating set of g the upper paired domination number of g denoted by pr g is the maximum cardinality of a minimal paired dominating set of g in upper pds it is required to compute a minimal paired dominating set with cardinality pr g for a given graph g in this paper we show that upper pds cannot be approximated within a factor of n 1 e for any e 0 unless p np and upper pds is apx complete for bipartite graphs of maximum degree 4 on the positive side we show that upper pds can be approximated within o factor for graphs with maximum degree we also show that upper pds is solvable in polynomial time for threshold graphs chain graphs and proper interval graphs

complexity of distance paired domination problem in graphs suppose g v e is a simple graph and k is a fixed positive integer a subset d v is a distancek dominating set of g if for every u v there exists a vertex v d such that d g u v k where d g u v is the distance between u and v in g a set d v is a distancek paired dominating set of g if d is a distance k dominating set and the induced subgraph g d contains a perfect matching given a graph g v e and a fixed integer k 0 the min distancek paired dom set problem is to find a minimum cardinality distance k paired dominating set of g in this paper we show that the decision version of min distancek paired dom set is np complete for undirected path graphs this strengthens the complexity of decision version of min distancek paired dom set problem in chordal graphs we show that for a given graph g unless np dtime n o l o g l o g n min distancek paired dom set problem cannot be approximated within a factor of 1 e lnn for any e 0 where n is the number of vertices in g we also show that min distancek paired dom set problem is apx complete for graphs with degree bounded by 3 on the positive side we present a linear time algorithm to compute the minimum cardinality of a distance k paired dominating set of a strongly chordal graph g if a strong elimination ordering of g is provided we show that for a given graph g min distancek paired dom set problem can be approximated with an approximation factor of 1 ln2 k ln d g where d g denotes the maximum degree of g

effect of nonstationarities on detrended fluctuation analysis detrended fluctuation analysis dfa is a scaling analysis method used to quantify long range power law correlations in signals many physical and biological signals are noisy heterogeneous and exhibit different types of nonstationarities which can affect the correlation properties of these signals we systematically study the effects of three types of nonstationarities often encountered in real data specifically we consider nonstationary sequences formed in three ways i stitching together segments of data obtained from discontinuous experimental recordings or removing some noisy and unreliable parts from continuous recordings and stitching together the remaining parts a cutting procedure commonly used in preparing data prior to signal analysis ii adding to a signal with known correlations a tunable concentration of random outliers or spikes with different amplitudes and iii generating a signal comprised of segments with different properties e g different standard deviations or different correlation exponents we compare the difference between the scaling results obtained for stationary correlated signals and correlated signals with these three types of nonstationarities we find that introducing nonstationarities to stationary correlated signals leads to the appearance of crossovers in the scaling behavior and we study how the characteristics of these crossovers depend on a the fraction and size of the parts cut out from the signal b the concentration of spikes and their amplitudes c the proportion between segments with different standard deviations or different correlations and d the correlation properties of the stationary signal we show how to develop strategies for preprocessing raw data prior to analysis which will minimize the effects of nonstationarities on the scaling properties of the data and how to interpret the results of dfa for complex signals with different local characteristics

multifractal moving average analysis and test of multifractal model with tuned correlations we address two common major problems in the study of time series characterizing fluctuations in complex systems multifractal analysis and multifractal modeling specifically we introduce a multi fractal centered moving average mf cma analysis which is computationally easier but equivalently performing compared with the well established multi fractal detrended fluctuation analysis mf dfa with linear detrending in addition we study in detail a generalized binomial multi fractal model gb mfm to conveniently and reliably generate multifractal surrogate data with arbitrary singularity strengths and arbitrary long term persistence we use the data generated by this model as well as realistic by construction monofractal data series with crossovers and trends to test and compare the multifractal analysis methods and discuss finite size effects as well as limitations due to spurious multifractality

multifractal properties of chinese stock market in shanghai abstract in this article we apply three methods of multifractal analysis partition function method singular spectrum method and multifractal detrended fluctuation analysis method to analyze the closing index fluctuations of shanghai stock market during the past seven years we have found that shanghai stock market has weak multifractal features and there are long range power law correlations between index series the shapes of singular spectrums do not change with time scales and their strengths weaken when the scales shorten but when the orders of partition function increase the strengths of multifractal increase the singular spectrums become rougher and the general hurst exponents decrease these results provide solid and important values for further study on the dynamic mechanism of stock market price fluctuation

correlations in economic time series a financial index of the new york stock exchange the sp here f is called the crossover frequency iii we confirm these results using the dfa detrended fluctuation analysis method see fig 2b which allows accurate estimates of exponents independent of local trends