Detection of low-dimensional chaos in buildings energy consumption time series

In this paper, nonlinear time series modeling techniques are applied to analyze building energy consumption data. The time series were obtained for the benchmark data set Proben 1, and comes from the first energy prediction contest, the Great Building Energy Predictor Shootout I, organized by ASHRAE. The phase space, which describes the evolution of the behavior of a nonlinear system, is reconstructed using the delay embedding theorem suggested by TAKENS. The embedding parameters, e.g. the delay time and the embedding dimension are estimated using the average mutual information (AMI) of the data and the false nearest neighbor (FNN) algorithm, respectively. Nonlinearity was detected, by applying the surrogate data sets method.Numerically estimated non-integral fractal dimension and a positive Lyapunov exponent are not necessarily sufficient indication of chaos; therefore we apply a more stringent criterion, developed by Gao and Zheng, which is based on the logarithmic displacement of time-dependent exponent curves, and show that these data are chaotic.Based on this analysis and proof, we then calculate the correlation dimension of the resulting attractor and the largest Lyapunov exponent. The correlation dimension 3.47 and largest Lyapunov exponent 0.047 are estimated. These results indicate that chaotic characteristics obviously exist in the specific energy consumption data set, and thus techniques based on phase space dynamics can be used to analyze and predict buildings energy use.     

Chaos and reproduction in sea level

Prediction of sea-level is an important task for navigation, coastal engineering and geodetic applications, as well as recreational activities. This study presents a comparison of Chaos theory and Auto-Regressive Integrated Moving Average (ARIMA) techniques for sea level modelling for daily, weekly, 10-day and monthly time scale at the Cocos (Keeling) islands from 1992 to 2001. The state space reconstruction of the unknown underlying process is directly employed from time series data, through Takens delay embedding method: optimal embedding dimension and delay time are obtained from false nearest neighbours and average mutual information techniques, respectively. Optimal values are then used for the estimation of the correlation dimension and the largest Lyapunov exponent, for inspecting possible signatures of chaotic dynamics. We find a positive Lyapunov exponent an evident feature of chaos. Indeed, the nonlinear prediction of sea level, in the period ranging from January 2001 to December 2001, is in an excellent agreement with the data for the same period, evidencing the nonlinear nature of the process. ARIMA method is also used for sea level modelling, for the same time scales; the performances of the two models are compared using such statistical indices as the root mean square error (RMSE) and correlation coefficient (CC). The comparative analyses show that the chaos theory model has a slight edge over ARIMA while both models are in principal acceptable.     

A probe into the chaotic nature of daily streamflow time series by correlation dimension and largest Lyapunov methods

Two chaotic indicators namely the correlation dimension and the Lyapunov exponent methods are investigated for the daily river flow of Kizilirmak River. A delay time of 60 days used for the reconstruction is chosen after examining the first minimum of the average mutual information of the data. The sufficient embedding dimension is estimated using the false nearest neighbor algorithm, which has a value of 11. Based on these embedding parameters the correlation dimension of the resulting attractor is calculated, as well as the average divergence rate of nearby orbits given by the largest Lyapunov exponent. The presence of chaos in the examined river flow time series is evident with the low correlation dimension (2.4) and the positive value of the largest Lyapunov exponent (0.0061).     

Nonlinear electronic circuit, Part I: Multiple routes to chaos

A nonlinear electronic oscillator, suitable for synchronized chaotic communication, is studied. This circuit is capable of transmitting discrete chaotic signals, although the chaotic modes of operation are controlled in an analog way. In Part I of this review paper the three routes to chaotic operation that appear, namely the period doubling, intermittency and crisis induced intermittency, are thoroughly studied and discussed. In all three routes to chaos the appropriate experimental distributions were calculated. Moreover, the chaotic nature of the circuit operation was evaluated by using the Grassberger-Procaccia method. Calculation of the corresponding minimum embedding dimension, the Kolmogorov-Sinai entropy as well as the maximal Lyapunov exponent give useful information in order to fully characterize the circuit operation.     

Nonlinear electronic circuit,Part I: Multiple routes to chaos

A nonlinear electronic oscillator, suitable for synchronized chaotic communication, is studied. This circuit is capable of transmitting discrete chaotic signals, although the chaotic modes of operation are controlled in an analog way. In Part I of this review paper the three routes to chaotic operation that appear, namely the period doubling, intermittency and crisis induced intermittency, are thoroughly studied and discussed. In all three routes to chaos the appropriate experimental distributions were calculated. Moreover, the chaotic nature of the circuit operation was evaluated by using the Grassberger–Procaccia method. Calculation of the corresponding minimum embedding dimension, the Kolmogorov–Sinai entropy as well as the maximal Lyapunov exponent give useful information in order to fully characterize the circuit operation.     

Posture as a chaotic system and an application to the Parkinson’s disease

In this work we investigate and compare a number of time series of stabilograms of healthy subjects and Parkinsonians. This is carried out by means of the chaos paradigm through the preliminary computation of the first minimum of the mutual information function and the embedding dimension (using false nearest neighbours) in order to obtain the correlation dimension as well as the largest Lyapunov exponent. We show that the postural act is indeed chaotic and especially that the latter two parameters do not allow to discriminate healthy subjects from parkinsonians. Moreover we report a discrepancy of our values with those found in previous works.     

原油期货价格的混沌识别研究

系统复杂性的研究是系统工程的一个热点研究领域。在虚假邻域概念基础上,给出了合适的嵌入参数的确定方法。讨论了分形维与最大Lyapunov指数的计算方法。纽约市场国际原油期货收盘价格时间序列数据的计算表明:这些数据来源于一最大Lyapunov指数值为0.038的混沌吸引子,混沌吸引子分形维为3.625,需用4个变量描述其所在系统的运动规律。此结论为进一步利用混沌理论研究原油期货价格的运动规律、进行相关的投资决策提供了重要信息。     

Research on characterization of wireless LANs traffic

In this paper, we employ actual wireless data that draw from well known archives of network traffic traces and investigate the characterization of the wireless LANs traffic. Firstly, useful preliminary information regarding the general wireless traffic dynamics is obtained using one standard statistical technique named Fourier power spectrum. Then the estimation of the parameters, such as the correlation dimension, the largest Lyapunov exponent and the principal components analysis indicate the existence of low-dimensional deterministic chaos in wireless traffic time series. Our results also show that the parameters selection of the phase space reconstruction influence the value of the correlation dimension and the largest Lyapunov exponent, but can not influence on diagnosis of chaotic nature of wireless traffic.     

Dimensions and Lyapunov exponents from exchange rate series

Detecting the presence of deterministic chaos in economic time series is an important problem that may be solved by measuring the largest Lyapunov exponent. In this paper we present estimates of the largest Lyapunov exponent in daily data for the Swedish Krona vs Deutsche Mark, ECU, U.S. Dollar and Yen exchange rates. In order to estimate the dimension of the systems producing these exchange rate series, we also present estimates of the correlation dimension. We found indications of deterministic chaos in all exchange rate series. However, the estimates for the largest Lyapunov exponents are not reliable, except in the Swedish Krona-ECU case, because of the limited number of data points. In the Swedish Krona-ECU case, we found indications of a low-order chaotic dynamical system.     

基于相空间重构技术的原油价格的时序混沌判据和混沌特征量

通过相空间重构技术,对Brent和WTI原油价格增长率的时间序列分别进行相空间重构,将若干固定时间延迟点上的数据作为新维处理,形成相点,应用Wolf方法得出了最大的Lyapunov指数,从而给出了系统混沌存在的证据;利用关联函数求出了关联维度和Kolmogorov熵,从而给出了对系统的混沌程度的估计和对Brent和WTI原油价格进行有效性预测的时间尺度.     

Interpreting supply chain dynamics: A quasi-chaos perspective

Chaotic phenomena, chaos amplification and other interesting nonlinear behaviors have been observed in supply chain systems. Chaos can be defined theoretically if the dynamics under study are produced only by deterministic factors. However, deterministic settings rarely present themselves in reality. In fact, real data are typically unknown. How can the chaos theory and its related methodology be applied in the real world? When the demand is stochastic, the interpretation and distribution of the Lyapunov exponents derived from the effective inventory at different supply chain levels are not similar to those under deterministic demand settings. Are the observed dynamics of the effective inventory random, chaotic, or simply quasi-chaos? In this study, we investigate a situation whereby the chaos analysis is applied to a time series as if its underlying structure, deterministic or stochastic, is unknown. The result shows clear distinction in chaos characterization between the two categories of demand process, deterministic vs. stochastic. It also highlights the complexity of the interplay between stochastic demand processes and nonlinear dynamics. Therefore, caution should be exercised in interpreting system dynamics when applying chaos analysis to a system of unknown underlying structure. By understanding this delicate interplay, decision makers have the better chance to tackle the problem correctly or more effectively at the demand end or the supply end.     

Chaos evidence in catecholamine secretion at chromaffin cells

Chromaffin cells secrete catecholamine molecules via exocytosis process. Each exocytotic event is characterized by a current spike, which corresponds to the amount of released catecholamine from secretory vesicles after fusing to plasma membrane. The current spike might be measured by the oxidation of catecholamine molecules and can be experimentally detected through amperometry technique. In this contribution, the secretion of catecholamine, namely adrenaline, of a set of bovine chromaffin cells is measured individually at each single cell. The aim is to study quantitative results of chaotic behavior in catecholamine secretion. For analysis, time series were obtained from amperometric measurements of each single chromaffin cell. Three analysis techniques were exploited: (i) A low-order attractor was generated by means of phase space reconstruction, Average Mutual Information (AMI) and False Nearest Neighbors (FNN) were used to compute embedding lag and embedding dimension, respectively. (ii) The properties of power spectrum density of time series were studied by Fast Fourier Transform (FFT) looking for possible dominant frequencies in power spectrum. (iii) Maximun Lyapunov Exponent (MLE) analysis was done to study the divergence of trajectories of the time series. Nevertheless, in order to dismiss the possibility of positiveness of MLE are due to the inherent noise in experiments, seven surrogate data sets computed using the Amplitude Adjusted Fourier Transform (AAFT) algorithm was computed. The phase space reconstruction showed that, in all cases, the trajectories lie in an embedding subspace suggesting oscillatory nature. The FFT analysis showed high dispersion of the power spectrum without any predominant frequency range. MLE analysis showed that the MLE values are positive for a given orbit time and a defined range of maximum scale values. Moreover, the trajectory of the MLE evolution of all the surrogate data are asymptotic and hold positive along the maximum scale range. These findings are preliminary evidence on detecting chaotic behavior in catecholamine secretion and, in general, their provide a first step towards a deeply understanding of nonlinear behavior of protein releasing dynamics.     

Bifurcations and chaos of a discrete mathematical model for respiratory process in bacterial culture

The discrete mathematical model for the respiratory process in bacterial culture obtained by Euler method is investigated. The conditions of existence for flip bifurcation and Hopf bifurcation are derived by using center manifold theorem and bifurcation theory, condition of existence of chaos in the sense of Marotto＇s definition of chaos is proved. The bifurcation diagrams, Lyapunov exponents and phase portraits are given for different parameters of the model, and the fractal dimension of chaotic attractor was also calculated. The numerical simulation results confirm the theoretical analysis and also display the new and complex dynamical behaviors compared with the continuous model. In particular~ we found that the new chaotic attractor, and new types of two or four coexisting chaotic attractors, and two coexisting invariant torus.     

基于Lyapunov指数的高炉铁水[Si]预报

通过对国内两座中型高炉冶炼过程的[S i]时间序列的混沌分析,计算出相应的Lyapunov指数谱.由最大Lyapunov指数为正,定量的说明了两座高炉冶炼过程具有混沌性,并估计了两座高炉冶炼过程[S i]可预测的时间尺度.同时根据最大Lyapunov指数,建立了高炉冶炼过程[S i]预报模型,取得了较好的结果.     

Novel method of identifying time series based on network graphs

In this article, we propose a novel method for transforming a time series into a complex network graph. The proposed algorithm is based on the spatial distribution of a time series. The characteristics of geometric parameters of a network represent the dynamic characteristics of a time series. Our algorithm transforms, respectively, a constant series into a fully connected graph, periodic time series into a regular graph, linear divergent time series into a tree, and chaotic time series into an approximately power law distribution network graph. We find that when the dimension of reconstructed phase space increases, the corresponding graph for a random time series quickly turns into a completely unconnected graph, while that for a chaotic time series maintains a certain level of connectivity. The characteristics of the generated network, including the total edges, the degree distribution, and the clustering coefficient, reflect the characteristics of the time series, including diverging speed, level of certainty, and level of randomness. This observation allows a chaotic time series to be easily identified from a random time series. The method may be useful for analysis of complex nonlinear systems such as chaos and random systems, by perceiving the differences in the outcomes of the systems—the time series—in the identification of the systemic levels of certainty or randomness. © 2011 Wiley Periodicals, Inc. Complexity, 2011     

Attractors of reaction‐diffusion systems in unbounded domains and their spatial complexity

The nonlinear reaction‐diffusion system in an unbounded domain is studied. It is proven that, under some natural assumptions on the nonlinear term and on the diffusion matrix, this system possesses a global attractor ?? in the corresponding phase space. Since the dimension of the attractor happens to be infinite, we study its Kolmogorov's ?‐entropy. Upper and lower bounds of this entropy are obtained. Moreover, we give a more detailed study of the attractor for the spatially homogeneous RDE in ?ⁿ. In this case, a group of spatial shifts acts on the attractor. In order to study the spatial complexity of the attractor, we interpret this group as a dynamical system (with multidimensional “time” if n > 1) acting on a phase space ??. It is proven that the dynamical system thus obtained is chaotic and has infinite topological entropy. In order to clarify the nature of this chaos, we suggest a new model dynamical system that generalizes the symbolic dynamics to the case of the infinite entropy and construct the homeomorphic (and even Lipschitz‐continuous) embedding of this system into the spatial shifts on the attractor. Finally, we consider also the temporal evolution of the spatially chaotic structures in the attractor and prove that the spatial chaos is preserved under this evolution. © 2003 Wiley Periodicals, Inc.     

Nonlinear dynamics of regenerative cutting processes—Comparison of two models

Understanding the nonlinear dynamics of cutting processes is essential for the improvement of machining technology. We study machine cutting processes by two different models, one has been recently introduced by Litak [Litak G. Chaotic vibrations in a regenerative cutting process. Chaos, Solitons & Fractals 2002;13:1531–5] and the other is the classic delay differential equation model. Although chaotic solutions have been found in both models, well known routes to chaos, such as period-doubling or quasi-periodic motion to chaos are not observed in either model. Careful analysis shows that the chaotic motion from the Litak’s model has sharper spectral peaks, a smaller correlation dimension and a smaller value for the largest positive Lyapunov exponent. Implications to the control of chaos in cutting processes are discussed.     

A Continuous Archetype of Nonuniform Chaos in Area-Preserving Dynamical Systems

We propose a piecewise linear, area-preserving, continuous map of the two-torus as a prototype of nonlinear two-dimensional mixing transformations that preserve a smooth measure (e.g., the Lebesgue measure). The model lends itself to a closed-form analysis of both statistical and geometric properties. We show that the proposed model shares typical features that characterize chaotic dynamics associated with area-preserving nonlinear maps, namely, strict inequality between the line-stretching exponent and the Lyapunov exponent, the dispersive behavior of stretch-factor statistics, the singular spatial distribution of expanding and contracting fibers, and the sign-alternating property of cocycle dynamics. The closed-form knowledge of statistical and geometric properties (in particular of the invariant contracting and dilating directions) makes the proposed model a useful tool for investigating the relationship between stretching and folding in bounded chaotic systems, with potential applications in the fields of chaotic advection, fast dynamo, and quantum chaos theory.     

High-speed image analysis reveals chaotic vibratory behaviors of pathological vocal folds

Laryngeal pathology is usually associated with irregular dynamics of laryngeal activity. High-speed imaging facilitates direct observation and measurement of vocal fold vibrations. However, chaotic dynamic characteristics of aperiodic high-speed image data have not yet been investigated in previous studies. In this paper, we will apply nonlinear dynamic analysis and traditional perturbation methods to quantify high-speed image data from normal subjects and patients with various laryngeal pathologies including vocal fold nodules, polyps, bleeding, and polypoid degeneration. The results reveal the low-dimensional dynamic characteristics of human glottal area data. In comparison to periodic glottal area series from a normal subject, aperiodic glottal area series from pathological subjects show complex reconstructed phase space, fractal dimension, and positive Lyapunov exponents. The estimated positive Lyapunov exponents provide the direct evidence of chaos in pathological human vocal folds from high-speed digital imaging. Furthermore, significant differences between the normal and pathological groups are investigated for nonlinear dynamic and perturbation analyses. Jitter in the pathological group is significantly higher than in the normal group, but shimmer does not show such a difference. This finding suggests that the traditional perturbation analysis should be cautiously applied to high speed image signals. However, the correlation dimension and the maximal Lyapunov exponent reveal a statistically significant difference between normal and pathological groups. Nonlinear dynamic analysis is capable of quantitatively describing the aperiodic vocal fold vibrations and may be helpful for understanding disordered behaviors in pathological laryngeal systems.     

Model based method for estimating an attractor dimension from uni/multivariate chaotic time series with application to Bremen climatic dynamics

In this paper, a method for estimating an attractor embedding dimension based on polynomial models and its application in investigating the dimension of Bremen climatic dynamics are presented. The attractor embedding dimension provides the primary knowledge for analyzing the invariant characteristics of the attractor and determines the number of necessary variables to model the dynamics. Therefore, the optimality of this dimension has an important role in computational efforts, analysis of the Lyapunov exponents, and efficiency of modeling and prediction. The smoothness property of the reconstructed map implies that, there is no self-intersection in the reconstructed attractor. The method of this paper relies on testing this property by locally fitting a general polynomial autoregressive model to the given data and evaluating the normalized one step ahead prediction error. The corresponding algorithms are developed in uni/multivariate form and some probable advantages of using information from other time series are discussed. The effectiveness of the proposed method is shown by simulation results of its application to some well-known chaotic benchmark systems. Finally, the proposed methodology is applied to two major dynamic components of the climate data of the Bremen city to estimate the related minimum attractor embedding dimension.