Projectile mass and energy dependence of multifractal detrended cross-correlation analysis of pseudorapidity space and azimuthal space in high energy nuclear collisions

2019 ◽  
Vol 28 (03) ◽  
pp. 1950012 ◽  
Author(s):  
Sucharita Chatterjee ◽  
Dipak Ghosh ◽  
Srimonti Dutta
Keyword(s):  
Experimental Data ◽  
Correlation Analysis ◽  
Long Range ◽  
Energy Dependence ◽  
Cross Correlation ◽  
High Energy ◽  
Long Range Correlations ◽  
Nuclear Collisions ◽  
Cross Correlation Analysis ◽  
The Cross

This paper studies the cross-correlation between the pseudorapidity and azimuthal distributions of the shower particles emitted in [Formula: see text]S-AgBr interactions at 200[Formula: see text]GeV and [Formula: see text]O-AgBr interactions at 60[Formula: see text]GeV applying Multifractal detrended cross-correlation analysis (MF-DXA) methodology. The cross-correlation between the pseudorapidity ([Formula: see text]) space and the azimuthal ([Formula: see text]) space is found to exhibit multifractality in case of both the interactions. The results obtained from the analysis of the experimental data were compared with those obtained for the randomly shuffled data for both the interactions, and the results revealed that the multifractality is due to the presence of long-range correlations. The study clearly indicates that the strength of the cross-correlation depends on both the projectile mass and energy.

scholarly journals Multifractal Detrended Cross-Correlation Analysis of Global Methane and Temperature

2020 ◽  
Vol 12 (3) ◽  
pp. 557 ◽  
Author(s):  
Chris G. Tzanis ◽  
Ioannis Koutsogiannis ◽  
Kostas Philippopoulos ◽  
Nikolaos Kalamaras
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Cross Correlation ◽  
Lower Troposphere ◽  
Fluctuation Analysis ◽  
Tropospheric Temperature ◽  
Long Range Correlations ◽  
Temperature Anomalies ◽  
Cross Correlation Analysis ◽  
The Cross

Multifractal Detrended Cross-Correlation Analysis (MF-DCCA) was applied to time series of global methane concentrations and remotely-sensed temperature anomalies of the global lower and mid-troposphere, with the purpose of investigating the multifractal characteristics of their cross-correlated time series and examining their interaction in terms of nonlinear analysis. The findings revealed the multifractal nature of the cross-correlated time series and the existence of positive persistence. It was also found that the cross-correlation in the lower troposphere displayed more abundant multifractal characteristics when compared to the mid-troposphere. The source of multifractality in both cases was found to be mainly the dependence of long-range correlations on different fluctuation magnitudes. Multifractal Detrended Fluctuation Analysis (MF-DFA) was also applied to the time series of global methane and global lower and mid-tropospheric temperature anomalies to separately study their multifractal properties. From the results, it was found that the cross-correlated time series exhibit similar multifractal characteristics to the component time series. This could be another sign of the dynamic interaction between the two climate variables.

Multiscale Multifractal Detrended Cross-Correlation Analysis of High-Frequency Financial Time Series

2019 ◽  
Vol 18 (03) ◽  
pp. 1950014 ◽  
Author(s):  
Jingjing Huang ◽  
Danlei Gu
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
High Frequency ◽  
Cross Correlation ◽  
Financial Time Series ◽  
Chinese Market ◽  
Change Of Scale ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross

In order to obtain richer information on the cross-correlation properties between two time series, we introduce a method called multiscale multifractal detrended cross-correlation analysis (MM-DCCA). This method is based on the Hurst surface and can be used to study the non-linear relationship between two time series. By sweeping through all the scale ranges of the multifractal structure of the complex system, it can present more information than the multifractal detrended cross-correlation analysis (MF-DCCA). In this paper, we use the MM-DCCA method to study the cross-correlations between two sets of artificial data and two sets of 5[Formula: see text]min high-frequency stock data from home and abroad. They are SZSE and SSEC in the Chinese market, and DJI and NASDAQ in the US market. We use Hurst surface and Hurst exponential distribution histogram to analyze the research objects and find that SSEC, SZSE and DJI, NASDAQ all show multifractal properties and long-range cross-correlations. We find that the fluctuation of the Hurst surface is related to the positive and negative of [Formula: see text], the change of scale range, the difference of national system, and the length of time series. The results show that the MM-DCCA method can give more abundant information and more detailed dynamic processes.

MULTIFRACTAL DETRENDED CROSS-CORRELATION ANALYSIS OF CHINESE STOCK MARKETS BASED ON TIME DELAY

Fractals ◽  
2011 ◽  
Vol 19 (03) ◽  
pp. 329-338 ◽  
Author(s):  
XIAOJUN ZHAO ◽  
PENGJIAN SHANG ◽  
QIUYUE JIN
Keyword(s):  
Time Delay ◽  
Correlation Analysis ◽  
Long Range ◽  
Power Law ◽  
Spectrum Analysis ◽  
Cross Correlation ◽  
Analysis Time ◽  
Singularity Spectrum ◽  
Stock Indices ◽  
Cross Correlation Analysis

Multifractal detrended cross-correlation analysis (MF-DXA) has been developed to detect the long-range power-law cross-correlation of two simultaneous series. However, the synchronization of underlying data can not be guaranteed integrated by a variety of factors. We artificially imbed a time delay in considered series and study its influence on the multifractal cross-correlation analysis. Time delay is found to affect the multifractal characterization, where a larger time delay causes a weaker multifractality. We also propose an alternative modification on MF-DXA to make the process more robust. The logarithmic return and volatility of Chinese stock indices show cross-correlation scaling behavior and strong multifractality by MF-DXA as well as singularity spectrum analysis.

MULTISCALE DETRENDED CROSS-CORRELATION ANALYSIS OF STOCK MARKETS

Fractals ◽  
2014 ◽  
Vol 22 (04) ◽  
pp. 1450007 ◽  
Author(s):  
YI YIN ◽  
PENGJIAN SHANG
Keyword(s):  
Correlation Analysis ◽  
Stock Markets ◽  
Cross Correlation ◽  
Statistical Characteristics ◽  
Detailed Knowledge ◽  
Stock Indices ◽  
Regional Markets ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross

In this paper, we employ the detrended cross-correlation analysis (DCCA) to investigate the cross-correlations between different stock markets. We report the results of cross-correlated behaviors in US, Chinese and European stock markets in period 1997–2012 by using DCCA method. The DCCA shows the cross-correlated behaviors of intra-regional and inter-regional stock markets in the short and long term which display the similarities and differences of cross-correlated behaviors simply and roughly and the persistence of cross-correlated behaviors of fluctuations. Then, because of the limitation and inapplicability of DCCA method, we propose multiscale detrended cross-correlation analysis (MSDCCA) method to avoid "a priori" selecting the ranges of scales over which two coefficients of the classical DCCA method are identified, and employ MSDCCA to reanalyze these cross-correlations to exhibit some important details such as the existence and position of minimum, maximum and bimodal distribution which are lost if the scale structure is described by two coefficients only and essential differences and similarities in the scale structures of cross-correlation of intra-regional and inter-regional markets. More statistical characteristics of cross-correlation obtained by MSDCCA method help us to understand how two different stock markets influence each other and to analyze the influence from thus two inter-regional markets on the cross-correlation in detail, thus we get a richer and more detailed knowledge of the complex evolutions of dynamics of the cross-correlations between stock markets. The application of MSDCCA is important to promote our understanding of the internal mechanisms and structures of financial markets and helps to forecast the stock indices based on our current results demonstrated the cross-correlations between stock indices. We also discuss the MSDCCA methods of secant rolling window with different sizes and, lastly, provide some relevant implications and issue.

Pipeline system identification through cross-correlation analysis

2002 ◽  
Vol 216 (3) ◽  
pp. 133-142 ◽  
Author(s):  
S B M Beck ◽  
N J Williamson ◽  
N D Sims ◽  
R Stanway
Keyword(s):  
Correlation Analysis ◽  
Cross Correlation ◽  
Correlation Technique ◽  
Pipeline System ◽  
Pipe Network ◽  
Cross Correlation Analysis ◽  
Valve Position ◽  
Single Sensor ◽  
The Cross ◽  
Cross Correlation Technique

The pipeline systems used to carry liquids and gases for the ventilation of buildings, water distributions networks, and the oil and chemical industries are usually monitored by a multiplicity of pressure, flow, and valve position sensors. By comparing the input signal to a valve with the pressure reading from the network using cross-correlation analysis, the technique described in this paper enables a single sensor to be used for monitoring. Specifically, the offset and gradient change of the cross-correlation function show the time delay between the input wave and the acquired output signal. These reflections arise from junctions, valves, and terminations, which can be located effectively using the cross-correlation technique. Investigations using a T-shaped pipe network have been conducted with a valve inserted in the pipeline to introduce artificial water hammer-type perturbations into the system. Both computational and experimental data are presented and the results are compared with the actual pipe network geometry. It is shown that it is possible to identify the location of various features of the network from the reflections and thus to perform either system characterisation or condition monitoring.

scholarly journals The cross-correlation analysis in Z source GX 349+2

2015 ◽  
Vol 455 (3) ◽  
pp. 2959-2968 ◽  
Author(s):  
G. Q. Ding ◽  
W. Y. Zhang ◽  
Y. N. Wang ◽  
Z. B. Li ◽  
J. L. Qu ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Cross Correlation ◽  
Cross Correlation Analysis ◽  
The Cross

scholarly journals EMD Method for Minimizing the Effect of Seasonal Trends in Detrended Cross-Correlation Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Keqiang Dong ◽  
You Gao ◽  
Nianpeng Wang
Keyword(s):  
Correlation Analysis ◽  
Long Range ◽  
Cross Correlation ◽  
Intrinsic Mode Functions ◽  
Nonstationary Signals ◽  
Seasonal Trends ◽  
Mode Decomposition ◽  
Cross Correlation Analysis ◽  
Correlated Signals ◽  
Emd Method

Detrended cross-correlation analysis (DCCA) is a scaling method commonly used to estimate long-range power-law cross-correlation in nonstationary signals. Recent studies have reported signals superimposed with trends, which often lead to the complexity of the signals and the susceptibility of DCCA. This paper artificially generates long-range cross-correlated signals and systematically investigates the effect of seasonal trends. Specifically, for the crossovers raised by trends, we propose a smoothing algorithm based on empirical mode decomposition (EMD) method which decomposes underlying signals into several intrinsic mode functions (IMFs) and a residual trend. After the removal of slowly oscillating components and residual term, seasonal trends are eliminated.

Analyzing the Cross-Correlation Between Onshore and Offshore RMB Exchange Rates Based on Multifractal Detrended Cross-Correlation Analysis (MF-DCCA)

2017 ◽  
Vol 16 (01) ◽  
pp. 1750004 ◽  
Author(s):  
Chi Xie ◽  
Yingying Zhou ◽  
Gangjin Wang ◽  
Xinguo Yan
Keyword(s):  
Correlation Analysis ◽  
Exchange Rates ◽  
Cross Correlation ◽  
Original Data ◽  
Sample Period ◽  
Cross Correlation Analysis ◽  
Us Dollar ◽  
The Cross ◽  
Multifractal Behavior ◽  
Rolling Windows

We use the multifractal detrended cross-correlation analysis (MF-DCCA) method to explore the multifractal behavior of the cross-correlation between exchange rates of onshore RMB (CNY) and offshore RMB (CNH) against US dollar (USD). The empirical data are daily prices of CNY/USD and CNH/USD from May 1, 2012 to February 29, 2016. The results demonstrate that: (i) the cross-correlation between CNY/USD and CNH/USD is persistent and its fluctuation is smaller when the order of fluctuation function is negative than that when the order is positive; (ii) the multifractal behavior of the cross-correlation between CNY/USD and CNH/USD is significant during the sample period; (iii) the dynamic Hurst exponents obtained by the rolling windows analysis show that the cross-correlation is stable when the global economic situation is good and volatile in bad situation; and (iv) the non-normal distribution of original data has a greater effect on the multifractality of the cross-correlation between CNY/USD and CNH/USD than the temporary correlation.

Sign in / Sign up

Export Citation Format

Share Document