Interpolating Strange Attractors via Fractional Brownian Bridges

We present a novel method for interpolating univariate time series data. The proposed method combines multi-point fractional Brownian bridges, a genetic algorithm, and Takens’ theorem for reconstructing a phase space from univariate time series data. The basic idea is to first generate a population of different stochastically-interpolated time series data, and secondly, to use a genetic algorithm to find the pieces in the population which generate the smoothest reconstructed phase space trajectory. A smooth trajectory curve is hereby found to have a low variance of second derivatives along the curve. For simplicity, we refer to the developed method as PhaSpaSto-interpolation, which is an abbreviation for phase-space-trajectory-smoothing stochastic interpolation. The proposed approach is tested and validated with a univariate time series of the Lorenz system, five non-model data sets and compared to a cubic spline interpolation and a linear interpolation. We find that the criterion for smoothness guarantees low errors on known model and non-model data. Finally, we interpolate the discussed non-model data sets, and show the corresponding improved phase space portraits. The proposed method is useful for interpolating low-sampled time series data sets for, e.g., machine learning, regression analysis, or time series prediction approaches. Further, the results suggest that the variance of second derivatives along a given phase space trajectory is a valuable tool for phase space analysis of non-model time series data, and we expect it to be useful for future research.     

浅海混响建模的声束跟踪理论

研究建立了基于声束跟踪理论的浅海混响强度计算方法和混响时间序列仿真方法。给出了混响强度计算的简要理论推导,并进行了模型计算值与实验值的比较。建立了一种混响时间序列仿真模型,给出了其实现框架和方法。结合实验数据与文献研究结果,进行了混响序列相关特性的检验与分析。结果表明:建立的混响强度计算模型能很好地进行浅海混响强度的预报,混响序列仿真模型能仿真具有不同包络分布的混响序列,且其相关特性符合实验与文献研究结果。      

Markov transition probability-based network from time series for characterizing experimental two-phase flow

We generate a directed weighted complex network by a method based on Markov transition probability to represent an experimental two-phase flow. We first systematically carry out gas-liquid two-phase flow experiments for measuring the time series of flow signals. Then we construct directed weighted complex networks from various time series in terms of a network generation method based on Markov transition probability. We find that the generated network inherits the main features of the time series in the network structure. In particular, the networks from time series with different dynamics exhibit distinct topological properties. Finally, we construct two-phase flow directed weighted networks from experimental signals and associate the dynamic behavior of gas-liquid two-phase flow with the topological statistics of the generated networks. The results suggest that the topological statistics of two-phase flow networks allow quantitative characterization of the dynamic flow behavior in the transitions among different gas-liquid flow patterns.     

Forecasting the underlying potential governing the time series of a dynamical system

We introduce a technique of time series analysis, potential forecasting, which is based on dynamical propagation of the probability density of time series. We employ polynomial coefficients of the orthogonal approximation of the empirical probability distribution and extrapolate them in order to forecast the future probability distribution of data. The method is tested on artificial data, used for hindcasting observed climate data, and then applied to forecast Arctic sea-ice time series. The proposed methodology completes a framework for ‘potential analysis’ of tipping points which altogether serves anticipating, detecting and forecasting nonlinear changes including bifurcations using several independent techniques of time series analysis. Although being applied to climatological series in the present paper, the method is very general and can be used to forecast dynamics in time series of any origin.     

基于极端学习机的多变量混沌时间序列预测

针对多变量混沌时间序列预测问题, 提出了一种基于输入变量选择和极端学习机的预测模型. 其基本思想是 对多变量混沌时间序列进行相空间重构后, 采用互信息方法选择与预测输出统计相关最高的重构输入变量, 借助极端学习机的通用逼近能力建立多变量混沌时间序列的预测模型. 为进一步提高预测精度, 采用模型选择算法选择具有最小期望风险的极端学习机预测模型. 基于Lorenz, Rössler多变量混沌时间序列及Rössler超混沌时间序列的仿 真结果证明所提方法的有效性.     

Generation of hierarchically correlated multivariate symbolic sequences

We introduce a method to generate multivariate series of symbols from a finite alphabet with a given hierarchical structure of similarities based on the Hamming distance. The target hierarchical structure of similarities is arbitrary, for instance the one obtained by some hierarchical clustering method applied to an empirical matrix of similarities. The method that we present here is based on a generating mechanism that does not make use of mutation rate, which is widely used in phylogenetic analysis. Here we use the proposed simulation method to investigate the relationship between the bootstrap value associated with a node of a phylogeny and the probability of finding that node in the true phylogeny. The results of this analysis are compared with those obtained in the literature according to an evolutionary model with a per-symbol constant mutation rate. We observe that the relationship between the bootstrap value of a node and the probability of the corresponding clade being correct is sensitive to both the length of data series and the length of the branch connecting the node to its closest ancestor in the phylogenetic tree, whereas such a relationship is only slightly affected by the topology of the true phylogeny and by the absolute value of similarity.     

Deep Prediction Model Based on Dual Decomposition with Entropy and Frequency Statistics for Nonstationary Time Series

The prediction of time series is of great significance for rational planning and risk prevention. However, time series data in various natural and artificial systems are nonstationary and complex, which makes them difficult to predict. An improved deep prediction method is proposed herein based on the dual variational mode decomposition of a nonstationary time series. First, criteria were determined based on information entropy and frequency statistics to determine the quantity of components in the variational mode decomposition, including the number of subsequences and the conditions for dual decomposition. Second, a deep prediction model was built for the subsequences obtained after the dual decomposition. Third, a general framework was proposed to integrate the data decomposition and deep prediction models. The method was verified on practical time series data with some contrast methods. The results show that it performed better than single deep network and traditional decomposition methods. The proposed method can effectively extract the characteristics of a nonstationary time series and obtain reliable prediction results.     

A psychophysical model of decision making

Herein we develop a psychophysical model of decision making based on the difference between objective clock time and the human brain’s perception of time. In this model the utility function is given by the survival probability, which is shown to be a generalized hyperbolic distribution. The parameters of the utility function are fit to intertemporal choice model experimental data and decision making is determined to be a 1/f-noise process.     

Time-Reversibility,Causality and Compression-Complexity

Detection of the temporal reversibility of a given process is an interesting time series analysis scheme that enables the useful characterisation of processes and offers an insight into the underlying processes generating the time series. Reversibility detection measures have been widely employed in the study of ecological, epidemiological and physiological time series. Further, the time reversal of given data provides a promising tool for analysis of causality measures as well as studying the causal properties of processes. In this work, the recently proposed Compression-Complexity Causality (CCC) measure (by the authors) is shown to be free of the assumption that the "cause precedes the effect", making it a promising tool for causal analysis of reversible processes. CCC is a data-driven interventional measure of causality (second rung on the Ladder of Causation) that is based on Effort-to-Compress (ETC), a well-established robust method to characterize the complexity of time series for analysis and classification. For the detection of the temporal reversibility of processes, we propose a novel measure called the Compressive Potential based Asymmetry Measure. This asymmetry measure compares the probability of the occurrence of patterns at different scales between the forward-time and time-reversed process using ETC. We test the performance of the measure on a number of simulated processes and demonstrate its effectiveness in determining the asymmetry of real-world time series of sunspot numbers, digits of the transcedental number π and heart interbeat interval variability.     

基于模糊模型支持向量机的混沌时间序列预测

基于支持向量机强大的非线性映射能力和模糊逻辑易于将先验的系统知识结合到模糊规则的 特性, 根据混沌动力系统的相空间重构理论, 提出了一种混沌时间序列的模糊模型的支持向 量机预测模型,并采用适用于大规模问题求解的最小二乘法来训练预测模型,利用该模型分别 对模型的整体预测性能与嵌入维数及延迟时间的关系进行了探讨.最后利用Mackey-Glass时 间序列和典型的Lorenz系统生成的时间序列对该模型进行了验证,结果表明该预测模型不仅 能够自动的从学习数据中获取知识产生模糊规则，提取能够代表混沌时间序列内在规律的支 持向量，大大减少支持向量的数目，精确地预测未来的混沌时间序列,而且在混沌时间序列 的嵌入维数未知和延迟时间不能合理选择的情况下,也能取得比较好的预测效果.这一结论预 示着基于模糊模型的支持向量机是一种研究混沌时间序列的有效方法. 关键词： 模糊模型 混沌时间序列 支持向量机 最小二乘法     

Prediction of chaotic time series based on modified minimax probability machine regression

Long-term prediction of chaotic time series is very difficult,for the Chaos restricts predictability.in this paper a new method is studied to model and predict chaotic time series based on minimax probability machine regression （MPMR）. Since the positive global Lyapunov exponents lead the errors to increase exponentially in modelling the chaotic time series, a weighted term is introduced to compensate a cost function. Using mean square error （MSE） and absolute error （AE） as a criterion, simulation results show that the proposed method is more effective and accurate for multistep prediction. It can identify the system characteristics quite well and provide a new way to make long-term predictions of the chaotic time series.[第一段]     

Inference of Kinetic Ising Model on Sparse Graphs

Based on dynamical cavity method, we propose an approach to the inference of kinetic Ising model, which asks to reconstruct couplings and external fields from given time-dependent output of original system. Our approach gives an exact result on tree graphs and a good approximation on sparse graphs, it can be seen as an extension of Belief Propagation inference of static Ising model to kinetic Ising model. While existing mean field methods to the kinetic Ising inference e.g., naïve mean-field, TAP equation and simply mean-field, use approximations which calculate magnetizations and correlations at time t from statistics of data at time t?1, dynamical cavity method can use statistics of data at times earlier than t?1 to capture more correlations at different time steps. Extensive numerical experiments show that our inference method is superior to existing mean-field approaches on diluted networks.     

A fluctuation problem in particle transport theory II

A balance equation is formulated for the probability that a particle injected into an infinite, amorphous medium will have suffered N collisions and have given rise to n new particles in a given energy range at time t. The method of regeneration points has been employed and this leads, in the case of two particle production, to a non-linear, integro-differential equation for the probability generating function. This equation is solved for the case of foreign particles slowing down, in which case it becomes linear and results are obtained which include the effects of electronic stopping and absorption, thus generalizing the work in part I. In the cascade problem, a single particle gives rise to two new particles in every collision and it is shown, for a simple hard-sphere model with 1/v scattering and absorption, how the non-linear equation may be solved. The probability for the number of particles and the number of collisions suffered to absorption is obtained in the case of zero absorption, the probability law is shown to obey a Furry distribution. The limitations of the method described in part I for dealing with cascades are highlighted.     

A long-range memory stochastic model of the return in financial markets

We present a nonlinear stochastic differential equation (SDE) which mimics the probability density function (PDF) of the return and the power spectrum of the absolute return in financial markets. Absolute return as a measure of market volatility is considered in the proposed model as a long-range memory stochastic variable. The SDE is obtained from the analogy with an earlier proposed model of trading activity in the financial markets and generalized within the nonextensive statistical mechanics framework. The proposed stochastic model generates time series of the return with two power law statistics, i.e., the PDF and the power spectral density, reproducing the empirical data for the one-minute trading return in the NYSE.     

Method of studying the synchronization of self-sustained oscillations using continuous wavelet analysis of univariant data

A new method for detecting the synchronization of a self-sustained oscillator under an external action with a linearly increasing frequency is proposed. It is based on the continuous wavelet transformation of univariant data (scalar time series). The efficiency of the method is demonstrated with a modified asymmetric van der Pol oscillator, Rössler oscillator, and experimental physiological data. In the last case, synchronization between rhythmic processes of different order in the cardiovascular and respiratory systems of the man is demonstrated using only the time series of the R-R intervals.     

Sub-Poisson statistics of subensembles of a micromaser field mode

It is proposed to use a result of indirect measurements—the average relative frequency of counts of a detector selective for the states of atoms escaping from a micromaser—for time selection of subensembles of a field mode. Analytical expressions for the reduced density matrix, average number of photons, and Mandel Q parameter for mode subensembles were obtained by the method of periodic paths. The generation of a random sequence of results of indirect quantum measurements was performed by the Monte Carlo method. The results of the analytical and numerical calculations are compared. It is found that the statistics of subensembles is sub-Poisson even when the calculation based on the matrix for the entire ensemble (in the absence of quantum measurements) gives super-Poisson statistics. It is noted that the time selection of subensembles is possible within a limited range of variation in the pumping parameter Θ, when the probability of quantum jumps is relatively low.     

线性非高斯序列的替代数据检验新方法

替代数据法作为检验时间序列非线性和混沌的统计方法获得了广泛的应用.由于原替代数据法的零假设为线性高斯过程,可能把线性非高斯过程,特别是非最小相位过程误判为非线性.为了解决这一问题,提出并详细推导了基于功率谱等价的非最小相位序列求逆方法;结合基于高阶累积量的非最小相位自回归滑动平均模型辨识方法,提出了检验序列是否为线性非高斯过程的替代数据生成新算法.仿真算例表明,上述方法成功地克服了原替代数据法的不足. 关键词： 替代数据 非线性检验 非最小相位 功率谱等价     

Effective noise estimation and filtering from correlated multiple-coil MR data

Modern magnetic resonance (MR) imaging protocols based on multiple-coil acquisitions have carried on a new attention to noise and signal statistical modeling, as long as most of the existing techniques for data processing are model based. In particular, nonaccelerated multiple-coil and GeneRalized Autocalibrated Partially Parallel Acquisitions (GRAPPA) have brought noncentral-χ (nc-χ) statistics into stake as a suitable substitute for traditional Rician distributions. However, this model is only valid when the signals received by each coil are roughly uncorrelated. The recent literature on this topic suggests that this is often not the case, so nc-χ statistics are in principle not adequate. Fortunately, such model can be adapted through the definition of a set of effective parameters, namely, an effective noise power (greater than the actual power of thermal noise in the Radio Frequency receiver) and an effective number of coils (smaller than the actual number of RF receiving coils in the system). The implications of these artifacts in practical algorithms have not been discussed elsewhere. In the present paper, we aim to study their actual impact and suggest practical rules to cope with them. We define the main noise parameters in this context, introducing a new expression for the effective variance of noise which is of capital importance for the two image processing problems studied: first, we propose a new method to estimate the effective variance of noise from the composite magnitude signal of MR data when correlations are assumed. Second, we adapt several model-based image denoising techniques to the correlated case using the noise estimation techniques proposed. We show, through a number of experiments with synthetic, phantom, and in vivo data, that neglecting the correlated nature of noise in multiple-coil systems implies important errors even in the simplest cases. At the same time, the proper statistical characterization of noise through effective parameters drives to improved accuracy (both qualitatively and quantitatively) for both of the problems studied.