arXiv subjects

Pei-Ling Zhou

Publications and source records attributed to Pei-Ling Zhou.

13 recordsLinked to original sources

arXiv2009

Scaling and memory in recurrence intervals of Internet traffic

By studying the statistics of recurrence intervals, $τ$, between volatilities of Internet traffic rate changes exceeding a certain threshold $q$, we find that the probability distribution functions, $P_{q}(τ)$, for both byte and packet flows, show scaling property as $P_{q}(τ)=\frac{1}{\overlineτ}f(\fracτ{\overlineτ})$. The scaling functions for both byte and packet flows obeys the same stretching exponential form, $f(x)=A\texttt{exp}(-Bx^β)$, with $β\approx 0.45$. In addition, we detect a strong memory effect that a short (or long) recurrence interval tends to be followed by another short (or long) one. The detrended fluctuation analysis further demonstrates the presence of long-term correlation in recurrence intervals.

physics.data-an↗

arXiv2007

Note on two phase phenomena in financial markets

The two phase behavior in financial markets actually means the bifurcation phenomenon, which represents the change of the conditional probability from an unimodal to a bimodal distribution. In this paper, the bifurcation phenomenon in Hang-Seng index is carefully investigated. It is observed that the bifurcation phenomenon in financial index is not universal, but specific under certain conditions. The phenomenon just emerges when the power-law exponent of absolute increment distribution is between 1 and 2 with appropriate period. Simulations on a randomly generated time series suggest the bifurcation phenomenon itself is subject to the statistics of absolute increment, thus it may not be able to reflect the essential financial behaviors. However, even under the same distribution of absolute increment, the range where bifurcation phenomenon occurs is far different from real market to artificial data, which may reflect certain market information.

q-fin.ST↗

arXiv2007

Scale-invariance of human EEG signals in sleep

We investigate the dynamical properties of electroencephalogram (EEG) signals of human in sleep. By using a modified random walk method, We demonstrate that the scale-invariance is embedded in EEG signals after a detrending procedure. Further more, we study the dynamical evolution of probability density function (PDF) of the detrended EEG signals by nonextensive statistical modeling. It displays scale-independent property, which is markedly different from the turbulent-like scale-dependent PDF evolution.

physics.bio-ph↗

arXiv2006

Scaling behavior of an artificial traffic model on scale-free networks

In this article, we investigate an artificial traffic model on scale-free networks. Instead of using the routing strategy of the shortest path, a generalized routing algorithm is introduced to improve the transportation throughput, which is measured by the value of the critical point disjoining the free-flow phase and the congested phase. By using the detrended fluctuation analysis, we found that the traffic rate fluctuation near the critical point exhibits the $1/f$-type scaling in the power spectrum. The simulation results agree very well with the empirical data, thus the present model may contribute to the understanding of the underlying mechanism of network traffics.

physics.soc-ph↗

arXiv2006

Diffusion entropy analysis on the stride interval fluctuation of human gait

In this paper, the diffusion entropy technique is applied to investigate the scaling behavior of stride interval fluctuations of human gait. The scaling behavior of the stride interval of human walking at normal, slow and fast rate are similar; with the scale-invariance exponents in the interval $[0.663,0.955]$, of which the mean value is $0.821\pm0.011$. Dynamical analysis of these stride interval fluctuations reveals a self-similar pattern: Fluctuation at one time scale are statistically similar to those at multiple other time scales, at least over hundreds of steps, while the healthy subjects walk at their normal rate. The long-range correlations are observed during the spontaneous walking after the removal of the trend in the time series with Fourier filter. These findings uncover that the fractal dynamics of stride interval of human gait are normally intrinsic to the locomotor systems.

physics.bio-ph↗

arXiv2006

Boolean Game on Scale-free Networks

Inspired by the local minority game, we propose a network Boolean game and investigate its dynamical properties on scale-free networks. The system can self-organize to a stable state with better performance than random choice game, although only the local information is available to the agent. By introducing the heterogeneity of local interactions, we find the system has the best performance when each agent's interaction frequency is linear correlated with its information capacity. Generally, the agents with more information gain more than those with less information, while in the optimal case, each agent almost has the same average profit. In addition, we investigate the role of irrational factor and find an interesting symmetrical behavior.

physics.soc-ph↗

arXiv2005

Diffusion entropy analysis on the scaling behavior of financial markets

In this paper the diffusion entropy technique is applied to investigate the scaling behavior of financial markets. The scaling behaviors of four representative stock markets, Dow Jones Industrial Average, Standard&Poor 500, Heng Seng Index, and Shang Hai Stock Synthetic Index, are almost the same; with the scale-invariance exponents all in the interval $[0.92, 0.95]$. These results provide a strong evidence of the existence of long-rang correlation in financial time series, thus several variance-based methods are restricted for detecting the scale-invariance properties of financial markets. In addition, a parsimonious percolation model for stock markets is proposed, of which the scaling behavior agrees with the real-life markets well.

physics.soc-ph↗

arXiv2005

Self-organized Boolean game on networks

A model of Boolean game with only one free parameter $p$ that denotes the strength of herd behavior is proposed where each agent acts according to the information obtained from his neighbors in network and those in the minority are rewarded. The simulation results indicate that the dynamic of system is sensitive to network topology, where the network of larger degree variance, i.e. the system of greater information heterogeneity, leads to less system profit. The system can self-organize to a stable state and perform better than random choice game, although only the local information is available to the agents. In addition, in heterogeneity networks, the agents with more information gain more than those with less information for a wide extent of herd strength $p$.

cond-mat.stat-mech↗

arXiv2005

Random Apollonian Networks

In this letter, we propose a simple rule that generates scale-free networks with very large clustering coefficient and very small average distance. These networks are called {\bf Random Apollonian Networks}(RANs) as they can be considered as a variation of Apollonian networks. We obtain the analytic result of power-law exponent $γ=3$ and clustering coefficient $C={46/3}-36\texttt{ln}{3/2}\approx 0.74$, which agree very well with the simulation results. We prove that the increasing tendency of average distance of RAN is a little slower than the logarithm of the number of nodes in RAN. Since many real-life networks are both scale-free and small-world, RANs may perform well in mimicking the reality. The epidemic spreading process is also studied, we find that the diseases spread slower in RANs than BA networks in the early stage of SI process, indicating that the large clustering coefficient may slower the spreading velocity especially in the outbreaks.

cond-mat.dis-nn↗

arXiv2004

Study on Evolvement Complexity in an Artificial Stock Market

An artificial stock market is established based on multi-agent . Each agent has a limit memory of the history of stock price, and will choose an action according to his memory and trading strategy. The trading strategy of each agent evolves ceaselessly as a result of self-teaching mechanism. Simulation results exhibit that large events are frequent in the fluctuation of the stock price generated by the present model when compared with a normal process, and the price returns distribution is Lévy distribution in the central part followed by an approximately exponential truncation. In addition, by defining a variable to gauge the "evolvement complexity" of this system, we have found a phase cross-over from simple-phase to complex-phase along with the increase of the number of individuals, which may be a ubiquitous phenomenon in multifarious real-life systems.

cond-mat.other↗

arXiv2004

Clustering Evolutionary Stock Market Model

As a typical representation of complex networks studied relatively thoroughly, financial market presents some special details, such as its nonconservation and opinions spreading. In this model, agents congregate to form some clusters, which may grow or collapse with the evolution of the system. To mimic an open market, we allow some ones participate in or exit the market suggesting that the number of the agents would fluctuate. Simulation results show that the large events are frequent in the fluctuations of the stock price generated by the artificial stock market when compared with a normal process and the price return distribution is a \emph{lévy} distribution in the central part followed by an approximately exponential truncation.

cond-mat.other↗

arXiv2004

Mathew Effect in Artificial Stock Market

In this article, we established a stock market model based on agents' investing mentality. The agents decide whether to purchase the shares at the probability, according to their anticipation of the market's behaviors. The expectation of the amount of shares they want to buy is directly proportional to the value of asset they hold. The agents sell their shares because of the gaining-profit psychology, stopping-loss psychology, or dissatisfaction with the long-time congealing of the assets. We studied how the distribution of agent's assets varies along with systemic evolution. The experiments show us obvious Mathew effect on asset distribution in the artificial stock market, and we have found that the Mathew effect on asset distribution was more and more salient along with the increasing of system running time, stock market size and agents' activity extent.

cond-mat.stat-mech↗

arXiv2004

Modeling Stock Market Based on Genetic Cellular Automata

An artificial stock market is established with the modeling method and ideas of cellular automata. Cells are used to represent stockholders, who have the capability of self-teaching and are affected by the investing history of the neighboring ones. The neighborhood relationship among the stockholders is the expanded Von Neumann relationship, and the interaction among them is realized through selection operator and crossover operator. Experiment shows that the large events are frequent in the fluctuations of the stock price generated by the artificial stock market when compared with a normal process and the price returns distribution is a Levy distribution in the central part followed by an approximately exponential truncation.

cond-mat.other↗