Features of the detrended cross-correlation analysis in the time series between absorbable particulate matter and meteorological factors

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.

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Scale Analysis and Correlation Study of Wildfire and the Meteorological Factors That Influence It

Mathematical Problems in Engineering ◽

10.1155/2018/5739805 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Jiazheng Lu ◽

Tejun Zhou ◽

Bo Li ◽

Chuanping Wu

Keyword(s):

Time Series ◽

Time Scale ◽

Detrended Fluctuation Analysis ◽

Cross Correlation ◽

Daily Precipitation ◽

Meteorological Factors ◽

Fluctuation Analysis ◽

Average Temperature ◽

Cross Correlation Analysis ◽

Detrended Fluctuation

Wildfire is a large-scale complex system. Insight into the mechanism that drives wildfires can be revealed by the distribution of the wildfire over a large time scale, which is one of the important topics in wildfire research. In this study, the scaling properties of four meteorological factors (relative humidity, daily precipitation, daily average temperature, and maximum wind speed) that can affect wildfires (number of wildfires per day) were investigated by using the detrended fluctuation analysis method. The results showed that the time series for these meteorological factors and wildfires have similar power exponents and turning points for the power exponents curve. The five types of time series have a lasting and steady long-range power law correlation over a certain time scale range, where the corresponding exponents were 0.6484, 0.5724, 0.8647, 0.7344, and 0.6734, respectively. They also have a reversible long-range power law correlation beyond a certain time scale, where the corresponding exponents are 0.3862, 0.2218, 0.1372, 0.2621, and 0.2678. The multifractal detrended fluctuation analysis results showed that the wildfire time series were multifractal. The results of the research based on the detrended cross-correlation analysis and the multifractal detrended cross-correlation analysis showed that relative humidity and daily precipitation have a considerable impact on the wildfire time series, while the impacts of daily average temperature and the maximum wind speed are relatively small. This study showed that identifying the factors causing the inherent volatility in the wildfire time series can improve understanding of the dynamic mechanism controlling wildfires and the meteorological parameters. These results can also be used to quantify the correlation between wildfire and the meteorological factors investigated in this study.

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Multifractal detrended cross-correlation analysis between PM2.5 and meteorological factors

Physica A Statistical Mechanics and its Applications ◽

10.1016/j.physa.2015.06.039 ◽

2015 ◽

Vol 438 ◽

pp. 114-123 ◽

Cited By ~ 48

Author(s):

Chen Zhang ◽

Zhiwei Ni ◽

Liping Ni

Keyword(s):

Correlation Analysis ◽

Cross Correlation ◽

Meteorological Factors ◽

Cross Correlation Analysis

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Importance of Body Sway Velocity Information in Controlling Ankle Extensor Activities During Quiet Stance

Journal of Neurophysiology ◽

10.1152/jn.00730.2002 ◽

2003 ◽

Vol 90 (6) ◽

pp. 3774-3782 ◽

Cited By ~ 201

Author(s):

Kei Masani ◽

Milos R. Popovic ◽

Kimitaka Nakazawa ◽

Motoki Kouzaki ◽

Daichi Nozaki

Keyword(s):

Time Series ◽

Correlation Analysis ◽

Muscle Activity ◽

Cross Correlation ◽

Gastrocnemius Muscle ◽

Quiet Stance ◽

Lateral Gastrocnemius ◽

Velocity Feedback ◽

Cross Correlation Analysis ◽

Velocity Information

In literature, it has been suggested that the CNS anticipates spontaneous change in body position during quiet stance and continuously modulates ankle extensor muscle activity to compensate for the change. The purpose of this study was to investigate whether velocity feedback contributes by modulating ankle extensor activities in an anticipatory fashion, facilitating effective control of quiet stance. Both theoretical analysis and experiments were carried out to investigate to what extent velocity feedback contributes to controlling quiet stance. The experiments were carried out with 16 healthy subjects who were asked to stand quietly with their eyes open or closed. During the experiments, the center of pressure (COP) displacement (COPdis), the center of mass (COM) displacement (COMdis), and COM velocity (COMvel) in the anteroposterior direction were measured. Rectified electromyograms (EMGs) were used to measure muscle activity in the right soleus muscle, the medial gastrocnemius muscle, and the lateral gastrocnemius muscle. The simulations were performed using an inverted pendulum model that described the anteroposterior kinematics and dynamics of quiet stance. In the simulations, an assumption was made that the COMdis of the body would be regulated using a proportional-derivative (PD) controller. Two different PD controllers were evaluated in these simulations: 1) a controller with the high-derivative/velocity gain (HDG) and 2) a controller with the low-derivative/velocity gain (LDG). Cross-correlation analysis was applied to investigate the relationships between time series obtained in experiments 1) COMdis and EMGs and 2) COMvel and EMGs. Identical cross-correlation analysis was applied to investigate the relationships between time series obtained in simulations 3) COMdis and ankle torque and 4) COMvel and ankle torque. The results of these analyses showed that the COMdis was positively correlated with all three EMGs and that the EMGs temporally preceded the COMdis. These findings agree with the previously published studies in which it was shown that the lateral gastrocnemius muscle is actively modulated in anticipation of the body's COM position change. The COMvel and all three EMGs were also correlated and the cross-correlation function (CCF) had two peaks: one that was positive and another that was negative. The positive peaks were statistically significant, unlike the negative ones; they were larger than the negative peaks; and their time shifts were much shorter compared with the time shifts of the negative peaks. When these results were compared with the CCF results obtained for simulated time series, it was discovered that the cross-correlation results for the HDG controller closely matched cross-correlation results for the experimental time series. On the other hand, the simulation result obtained for LDG controller did not match the experimental results. These findings suggest that the actual postural control system during quiet stance adopts a control strategy that relies notably on velocity information and that such a controller can modulate muscle activity in anticipatory manner without using a feed-forward mechanism.

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