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.

Comparison of Multiscale Methods in the Stock Markets for Detrended Cross-correlation Analysis and Cross-sample Entropy

2014 ◽  
Vol 13 (03) ◽  
pp. 1450023 ◽  
Author(s):  
Yi Yin ◽  
Pengjian Shang
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Stock Markets ◽  
Cross Correlation ◽  
Sample Entropy ◽  
Multiscale Methods ◽  
Market Mechanisms ◽  
Cross Correlation Analysis ◽  
The Cross ◽  
The Difference

In this paper, we employ multiscale cross-sample entropy (MSCE), multiscale detrended cross-correlation analysis (MSDCCA) and DCCA cross-correlation coefficient (σDCCA) measurement to investigate the relationship between time series among different stock markets. We report the results of synchronism and cross-correlation behaviors in US and Chinese stock markets by these three methods. It can be concluded that the MSCE analysis point out the similarity on the cross-correlation among the stock markets while the MSCE makes it difficult to distinguish the indices in the same region and identify the difference and uniqueness of stock markets. However, both the MSDCCA analysis and σDCCA analysis reflect the similarity and uniqueness on the cross-correlation behaviors and reach the consistency. Furthermore, MSDCCA gives detailed multiscale cross-correlation structures and show some new interesting characteristics and conclusions, while the multiscale analysis by σDCCA provides a large amount of information on the cross-correlations and quantifies the level of cross-correlation more clearly and intuitively. MSDCCA and σDCCA methods may be more proper measures for the investigation of the cross-correlation between time series. We believe that such researches are relevant for a better understanding of the stock market mechanisms, and may lead to a better forecasting of the stock indices.

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 Higher-Order Multifractal Detrended Partial Cross-Correlation Analysis for the Correlation Estimator

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Keqiang Dong ◽  
Xiaojie Gao
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Correlation Coefficient ◽  
Nonlinear Interaction ◽  
Cross Correlation ◽  
Nonstationary Time Series ◽  
New Method ◽  
Nonlinear Interactions ◽  
Cross Correlation Analysis ◽  
The Cross

In this paper, we develop a new method to measure the nonlinear interactions between nonstationary time series based on the detrended cross-correlation coefficient analysis. We describe how a nonlinear interaction may be obtained by eliminating the influence of other variables on two simultaneous time series. By applying two artificially generated signals, we show that the new method is working reliably for determining the cross-correlation behavior of two signals. We also illustrate the application of this method in finance and aeroengine systems. These analyses suggest that the proposed measure, derived from the detrended cross-correlation coefficient analysis, may be used to remove the influence of other variables on the cross-correlation between two simultaneous time series.

scholarly journals Persistent Scatterer Interferometry and Statistical Analysis of Time-Series for Landslide Monitoring: Application to Santo Stefano d’Aveto (Liguria, NW Italy)

2021 ◽  
Vol 13 (17) ◽  
pp. 3348
Author(s):  
Evandro Balbi ◽  
Martino Terrone ◽  
Francesco Faccini ◽  
Davide Scafidi ◽  
Simone Barani ◽  
...  
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Urban Areas ◽  
Cross Correlation ◽  
Persistent Scatterer Interferometry ◽  
Persistent Scatterer ◽  
Cross Correlation Analysis ◽  
Santo Stefano ◽  
The Cross ◽  
Nw Italy

Landslides are a major threat for population and urban areas. Persistent Scatterer Interferometry (PSI) is a powerful tool for identifying landslides and monitoring their evolution over long periods and has proven to be very useful especially in urban areas, where a sufficient number of PS can be generated. In this study, we applied PS interferometry to investigate the landslide affecting Santo Stefano d’Aveto (Liguria, NW Italy) by integrating classic interferometric techniques with cross-correlation analysis of PS time-series and with geological and geotechnical field information. We used open-source software and packages to process Synthetic Aperture Radar (SAR) images from the Copernicus Sentinel-1A satellite for both ascending and descending orbits over the period 2015–2021 and calculate both the vertical motion and the E-W horizontal displacement. By computing the cross-correlation of the PS time-series, we identified three families of PS with a similarity greater than 0.70. The cross-correlation analysis allowed subdividing the landslide in different sectors, each of which is characterized by a specific type of movement. The geological meaning of this subdivision is still a matter of discussion but it is presumably driven by the geomorphological setting of the area and by the regional tectonics.

Multifractal Fluctuation Analysis of Correlations Between the Sector Stock Markets in China and the US

2020 ◽  
pp. 2150031
Author(s):  
You-Shuai Feng ◽  
Hong-Yong Wang
Keyword(s):  
Correlation Analysis ◽  
Stock Markets ◽  
Cross Correlation ◽  
Rapid Development ◽  
Fluctuation Analysis ◽  
Cross Correlation Analysis ◽  
The Us ◽  
Cross Correlations ◽  
The Cross ◽  
Stock Indexes

With the rapid development of economic globalization, the stock markets in China and the US are increasingly linked. The fluctuation features and cross-correlations of the two countries’ markets have attracted extensive attention from market investors and researchers. In this paper, the fractal analysis methods including multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) and coupled detrended cross-correlation analysis (CDCCA) are applied to explore the volatilities of CSI300 and SP500 sector stock indexes as well as the cross-correlations and coupling cross-correlations between the two corresponding sector stock indexes. The results show that the auto-correlations, cross-correlations and coupling cross-correlations have multifractal fluctuation characteristics, and that the cross-correlations are asymmetric. Additionally, the coupling cross-correlation strengths are distinct due to the different influence of long-range correlations and fat-tailed distribution. Further, the co-movement between China and the US sector stock markets is susceptible to external market factors such as major economic events and national policies.

Time Series Cross Correlation Analysis among Pollution Source and Adjacent Marine Water Environmental Factors

2014 ◽  
Vol 522-524 ◽  
pp. 56-59
Author(s):  
Ming Chang Li ◽  
Ying Jie Zhao
Keyword(s):  
Time Series ◽  
Environmental Factors ◽  
Correlation Analysis ◽  
Cross Correlation ◽  
Pollution Source ◽  
Marine Water ◽  
Computation Method ◽  
Correlation Relationship ◽  
Cross Correlation Analysis ◽  
The Cross

The cross correlation analysis of pollution time series among pollution source and adjacent marine water environmental factors is an essential tool for obtaining the relationship in adjacent marine waters and the source of pollution. Meanwhile, the main pollution source should be obtained by this analysis. In this paper, the cross correlation of the dissolved inorganic nitrogen (DIN) in the Caofeidian marine district, the Beidaihe marine district, Tangshan Bay and the whole quantity of pollution in main rivers of Hebei Province is analyzed. The cross correlation coefficient computation method is used for the correlation. The research results show that the stronger correlation relationship exists between the pollution source and the Beidaihe marine district, owing to the influence of the Luanhe river.

Sign in / Sign up

Export Citation Format

Share Document