Modified generalized multiscale sample entropy and surrogate data analysis for financial time series

Nonlinear Dynamics - Complexity in time series is an intriguing feature of living dynamical systems such as financial systems, with potential use for identification of system state. Multiscale...     

Weighted link entropy and multiscale weighted link entropy for complex time series

Nonlinear Dynamics - In the presented paper, an analytical approach based on the Harmonic Balance method is used to evaluate amplitudes of higher-order harmonics generated due to the interaction of...     

Weighted multiscale permutation entropy of financial time series

In this paper, we propose the weighted multiscale permutation entropy (WMPE) as a novel measure to quantify the complexity of nonlinear time series containing amplitude-coded information. WMPE is different from multiscale permutation entropy (MPE) in the sense that it suits better signals having considerable amplitude information and also succeed in accounting for the multiple time scales inherent in the financial systems. We first perform the MPE and WMPE methods on synthetic data showing the power of WMPE. Then, we apply the MPE and WMPE methods to the US and Chinese stock markets and make a comparison to discuss their differences and similarities between these different markets. The WMPE of each US and Chinese stock market points out the necessity of applying permutation entropy on multiple scales and reveals amplitude-coded information contained in the signals and immunity to degradation by noise when \(m = 5-7\) . Some new conclusions are gotten by the new characteristics we detected in the comparison. WMPE method can distinguish ShangZheng and ShenCheng from these markets and imply that HSI is more similar to the US markets. WMPE moves the fluctuation range of entropy values of different market down differently reflecting more accurate complexity of different stock markets containing amplitude information. Compared WMPE of ShangZheng and ShenCheng with the WMPE of US markets and HSI, US stock market and HSI may have more amplitude information carried by the signals of stock market. Furthermore, compared with MPE, WMPE can reduce the standard deviation which ensures the results more robust. The conclusion that \(m = 7\) is the best embedding dimension to investigate the WMPE results can be drawn because the WMPE tends to be stable in a certain range and reflects the necessity of investigation on multiscale and advantages of adding different weight, and it can distinguish these markets while reducing the standard deviations of all the markets.     

Complexity analysis of multiscale multivariate time series based on entropy plane via vector visibility graph

Nonlinear Dynamics - Vector visibility graph (VVG) is an algorithm that transforms multivariate time series into directed complex networks. However, at present, the researches of VVG mainly focus...     

Detecting asynchrony of two series using multiscale cross-trend sample entropy

Nonlinear Dynamics - We develop a new cross-sample entropy, namely the multiscale cross-trend sample entropy (MCTSE), to investigate the synchronism of dynamical structure regarding two series with...     

Topological entropy and geometric entropy and their application to the horizontal visibility graph for financial time series

Seismic wave control is very important both in civil and mechanical engineering. Common passive methods for isolating a building or a device include base isolators and tuned mass dampers. In the present paper, a time-varying controllable spring is considered as a vibration isolator for a linear mechanical system. The controller works as follows: When the seismic movement is active, the velocity of the moving mass is monitored as the reference velocity. When such reference velocity is positive, the stiffness is reduced; when it is negative, the stiffness is increased. Numerical investigations show that the controller is capable of filtering seismic excitation close to the natural frequency of the controlled system and reducing the total seismic energy transfer up to 5 times. The role played by the gravity in the active vibration filtering is pointed out by showing that no filtering action can be observed in gravity-free simulations. Moreover, control effectiveness has been proven for a measured seismic signal, showing its robustness in presence of noise.     

Multiscale cumulative residual distribution entropy and its applications on heart rate time series

Nonlinear Dynamics - Distribution entropy has been proved to reveal stability for short time series and to distinguish different classes of series by complexity. However, there still exists some...     

Novel techniques for improving NNetEn entropy calculation for short and noisy time series

Entropy is a fundamental concept in the field of information theory. During measurement, conventional entropy measures are susceptible to length and amplitude changes in time series. A new entropy metric, neural network entropy (NNetEn), has been developed to overcome these limitations. NNetEn entropy is computed using a modified LogNNet neural network classification model. The algorithm contains a reservoir matrix of N = 19,625 elements that must be filled with the given data. A substantial number of practical time series have fewer elements than 19,625. The contribution of this paper is threefold. Firstly, this work investigates different methods of filling the reservoir with time series (signal) elements. The reservoir filling method determines the accuracy of the entropy estimation by convolution of the study time series and LogNNet test data. The present study proposes 6 methods for filling the reservoir for time series of any length 5 ≤ N ≤ 19,625. Two of them (Method 3 and Method 6) employ the novel approach of stretching the time series to create intermediate elements that complement it, but do not change its dynamics. The most reliable methods for short-time series are Method 3 and Method 5. The second part of the study examines the influence of noise and constant bias on entropy values. In addition to external noise, the hyperparameter (bias) used in entropy calculation also plays a critical role. Our study examines three different time series data types (chaotic, periodic, and binary) with different dynamic properties, Signal-to-Noise Ratio (SNR), and offsets. The NNetEn entropy calculation errors are less than 10% when SNR is greater than 30 dB, and entropy decreases with an increase in the bias component. The third part of the article analyzes real-time biosignal EEG data collected from emotion recognition experiments. The NNetEn measures show robustness under low-amplitude noise using various filters. Thus, NNetEn measures entropy effectively when applied to real-world environments with ambient noise, white noise, and 1/f noise.

     

A new method for characterizing patterns of neural spike trains and its application

A method for characterizing and identifying firing patterns of neural spike trains is presented. Based on the time evolution of a neural spike train, the counting process is constructed as a time-dependent stair-like function. Three characteristic variables defined at sequential moments, including two formal derivatives and the integration of the counting process, are introduced to reflect the temporal patterns of a spike train. The reconstruction of a spike train with these variables verify the validity of this method. And a model of cold receptor is used as an example to investigate the temporal patterns under different temperature conditions. The most important contribution of our method is that it not only can reflect the features of spike train patterns clearly by using their geometrical properties, but also it can reflect the trait of time, especially the change of bursting of spike train. So it is a useful complementarity to conventional method of averaging.     

Weighted dynamic transfer network and spectral entropy for weak nonlinear time series detection

Nonlinear Dynamics - This work proposes a novel method to construct weighted dynamic transfer network to map time series to complex networks. Firstly, we map time series into symbolic series by...     

A new N-body potential and its application

Based on the embedded atom method (EAM) proposed by Daw and Baskes and Johnson's model, this paper constructs a new N-body potential for bcc crystal Mo. The procedure of constructing the new N-body potential can be applied to other metals. The dislocation emission from a crack tip has been simulated successfully using molecular dynamics method, the result is in good agreement with the elastic solution. The project supported by the National Science Foundation of China     

A multiscale method for dislocation nucleation and seamlessly passing scale boundaries

In the concurrent multiscale analysis, it is difficult to have truly seamless transition between the atomistic and continuum scale. This situation is even worse when defects pass through the boundary between different scales. For example, there is a lack of effective methods to handle the dislocation passing through scale boundaries which is important to investigate plasticity at the nanoscale. In this paper, the generalized particle (GP) method proposed by the first author is further developed so that a seamless transition and dislocation passing between different scales can be realized. Specifically, the linkage between different scales is through material neighbor-link cells (NLC) with scale duality. This indicates that material elements can be high-scale particles through a lumping process and can also be atoms via decomposition depending on the needs of the simulation. At the interface, the information transfer from bottom scale-up or from top scale-down is through the particles or atoms in the NLC. They are with the same material structure, all possess nonlocal constitutive behavior; thus, the smooth transition at the interface between different scales can be attained and validated to avoid non-physical responses. To save degrees of freedom, atoms are lumped together into a generalized particle in the domain in which the deformation gradient is near homogeneous. On the other hand, when defects such as dislocations in the atomistic domain are near the particle domain, the particles along dislocation propagation path and its surrounding region will be decomposed into atoms so dislocations can freely pass through the scale boundary and propagate inside the model just as it propagates in the deformed atomistic crystal structure. The method is verified first for seamless transition of variables at the scale boundary by a one-dimensional model and then verified for dislocation nucleation and propagation passing through scale boundaries in two cases, one is near the free surface and the other is inside of the copper nanowire. All the validations are through comparisons with fully atomistic analyses under same conditions. The comparison is satisfactory.     

Wavelet series method for reconstruction and spectral estimation of laser Doppler velocimetry data

Many techniques have been developed in order to obtain spectral density function from randomly sampled data, such as the computation of a slotted autocovariance function. Nevertheless, one may be interested in obtaining more information from laser Doppler signals than a spectral content, using more or less complex computations that can be easily conducted with an evenly sampled signal. That is the reason why reconstructing an evenly sampled signal from the original LDV data is of interest. The ability of a wavelet-based technique to reconstruct the signal with respect to statistical properties of the original one is explored, and spectral content of the reconstructed signal is given and compared with estimated spectral density function obtained through classical slotting technique. Furthermore, LDV signals taken from a screeching jet are reconstructed in order to perform spectral and bispectral analysis, showing the ability of the technique in recovering accurate information’s with only few LDV samples.     

A new method for Fourier series expansions: Applications in rotor-seal systems

Complex nonlinearities of rotor-seal systems make it difficult to implement some widely used techniques for nonlinear vibrations. This is partly due to the fact that it is rather cumbersome to expand the nonlinear terms into Fourier series. In this paper, a novel Fourier series expansion method is proposed to circumvent this difficulty. The incremental harmonic balance method is utilized to obtain the solutions of a rotor-seal system, where the complicated nonlinearities are handled by the expansion method. Periodic, double-periodic and triple-periodic solutions are obtained in excellent agreement with numerical results, which shows the validity and efficacy of the proposed solution procedures.     

The effects of the irregular sample and missing data in time series analysis

Human self-report time series data are typically marked by irregularities in sampling rates; furthermore, these irregularities are typically natural outcomes of the data generation process. Relatively little has been published to assist the analysis of irregularly sampled data. We report the results of a series of computational experiments on synthetic data sets designed to assess the utility of techniques for handling irregular time series data. The behavior of a conservative quasiperiodic, a dissipative chaotic, and a self-organized critical dynamics were sampled regularly in time and the regular sampling was disrupted by data point removal or by stochastic shifts in time. Missing data segments were then patched by means of segment concatenation, by segment filling with average data values, or by local interpolation in phase space. We compared results of nonlinear analytical tools such as autocorrelations and correlation dimensions using complete and patched sets, as well as power spectra with Lomb periodograms of the decimated sets. Local interpolation in phase space was particularly successful at preserving key features of the original data, but required potentially impractical quantities of intact data as a primer. While the other patching methods are not limited by the need for intact data, they distort results relative to the intact series. We conclude that irregularly sampled data sets with as much as 15 percent missing data can potentially be re-sampled or repaired for analysis with techniques that assume regular sampling without introducing substantial errors.     

Modified dynamic minimization algorithm for parameter estimation of chaotic system from a time series

This paper proposes a modified dynamic minimization algorithm for parameter estimation of chaotic systems, based on a scalar time series. Comparing with the previous design proposed by Maybhate and Amritkar (Phys. Rev. E 59:284?C293, 1999), two important new design concepts related to the feedback control and the auxiliary functions for parametric updating laws are introduced. Two different types of estimates can then be derived, and numerical simulations confirm their superior performances to the designs based on the original dynamic minimization algorithm or other existing approaches. Furthermore, a circuit experiment is carried out to demonstrate the robustness and practicability of the proposed design.     

A new method for the numerical solution of time dependent viscous flow

A new method of solving numerically the Navier-Stokes equations for time dependent flow is presented. The velocity components and pressure are used as the main variables, but in order to ensure continuity on a global scale, the Stokes stream function is also employed. The solution process is formulated in cylindrical polar co-ordinates and both implicit and explicit versions are discussed. Time dependent development in the entrance region of a cylindrical pipe is investigated using the method and several interesting flow features are predicted.