The Vegetation–Climate System Complexity through Recurrence Analysis

Multiple studies revealed that pasture grasslands are a time-varying complex ecological system. Climate variables regulate vegetation growing, being precipitation and temperature the most critical driver factors. This work aims to assess the response of two different Vegetation Indices (VIs) to the temporal dynamics of temperature and precipitation in a semiarid area. Two Mediterranean grasslands zones situated in the center of Spain were selected to accomplish this goal. Correlations and cross-correlations between VI and each climatic variable were computed. Different lagged responses of each VIs series were detected, varying in zones, the year’s season, and the climatic variable. Recurrence Plots (RPs) and Cross Recurrence Plots (CRPs) analyses were applied to characterise and quantify the system’s complexity showed in the cross-correlation analysis. RPs pointed out that short-term predictability and high dimensionality of VIs series, as well as precipitation, characterised this dynamic. Meanwhile, temperature showed a more regular pattern and lower dimensionality. CRPs revealed that precipitation was a critical variable to distinguish between zones due to their complex pattern and influence on the soil’s water balance that the VI reflects. Overall, we prove RP and CRP’s potential as adequate tools for analysing vegetation dynamics characterised by complexity.

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Dynamic Cross-Correlations between Investors’ Attention and CSI300 Index Futures

Fluctuation and Noise Letters ◽

10.1142/s0219477519500226 ◽

2019 ◽

Vol 18 (04) ◽

pp. 1950022

Author(s):

Xiong Xiong ◽

Kewei Xu ◽

Dehua Shen

Keyword(s):

Cross Correlation ◽

Futures Market ◽

Relative Activity ◽

Short Term ◽

Index Futures ◽

Search Volume ◽

Cross Correlation Analysis ◽

Cross Correlations ◽

The Cross

Using search volume on Baidu Index as the proxy for investors’ attention, we investigate the dynamic nonlinear relationship between investors’ attention and CSI300 index futures market. Multifractal detrend cross-correlation analysis (MF-DCCA) is employed to explore the multifractal features of the cross-correlations between investors’ attention and the return and relative activity of index futures market. We find that the power-law cross-correlations between investors’ attention and CSI300 index futures market are stronger in the short term than in the long term, and the cross-correlations are significantly multifractal. Precisely, the cross-correlation between abnormal search volume (ASV) and the relative activity is persistent, and the cross-correlation between ASV and return of IF is persistent in the short term but weakly anti-persistent in the long term. Besides, we also find that, with the restriction on index futures market, the cross-correlations between investors’ attention and CSI300 index futures market become less stable.

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Cross-Correlations between Energy and Emissions Markets: New Evidence from Fractal and Multifractal Analysis

Mathematical Problems in Engineering ◽

10.1155/2014/197069 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13 ◽

Cited By ~ 5

Author(s):

Gang-Jin Wang ◽

Chi Xie ◽

Shou Chen ◽

Feng Han

Keyword(s):

Multifractal Analysis ◽

Cross Correlation ◽

Market Dynamics ◽

Gas Oil ◽

Short Term ◽

Cross Correlation Analysis ◽

Cross Correlations ◽

Rolling Windows ◽

Oil Gas

We supply a new perspective to describe and understand the behavior of cross-correlations between energy and emissions markets. Namely, we investigate cross-correlations between oil and gas (Oil-Gas), oil and CO2(Oil-CO2), and gas andCO2(Gas-CO2) based on fractal and multifractal analysis. We focus our study on returns of the oil, gas, andCO2during the period of April 22, 2005–April 30, 2013. In the empirical analysis, by using the detrended cross-correlation analysis (DCCA) method, we find that cross-correlations for Oil-Gas, Oil-CO2, and Gas-CO2obey a power-law and are weakly persistent. Then, we adopt the method of DCCA cross-correlation coefficient to quantify cross-correlations between energy and emissions markets. The results show that their cross-correlations are diverse at different time scales. Next, based on the multifractal DCCA method, we find that cross-correlated markets have the nonlinear and multifractal nature and that the multifractality strength for three cross-correlated markets is arranged in the order of Gas-CO2 > Oil-Gas > Oil-CO2. Finally, by employing the rolling windows method, which can be used to investigate time-varying cross-correlation scaling exponents, we analyze short-term and long-term market dynamics and find that the recent global financial crisis has a notable influence on short-term and long-term market dynamics.

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Dynamic Cross-Correlations between Participants’ Attentions to P2P Lending and Offline Loan in the Private Lending Market

Complexity ◽

10.1155/2019/1635793 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8

Author(s):

Yingxiu Zhao ◽

Wei Zhang ◽

Xiangyu Kong

Keyword(s):

Correlation Analysis ◽

Power Law ◽

Cross Correlation ◽

Granger Causality Test ◽

Short Term ◽

Causality Test ◽

Cross Correlation Analysis ◽

Private Lending ◽

P2p Lending ◽

Cross Correlations

In this paper, we examine the dynamic cross-correlations between participants’ attentions to the P2P lending and offline loan (lending) with the method of multifractal detrended cross-correlation analysis (MF-DCCA). The empirical result mainly shows that (1) the power-law cross-correlation exists between participants’ attentions to the P2P lending and offline loan and is persistent, (2) the cross-correlation is more stable in the short term, and (3) the relation subjected to a small fluctuation is more cross-correlated than that under larger ones. Furthermore, we carry out the robustness test to verify the result. The Granger causality test indicates that participants’ attentions to P2P lending and offline loan Granger cause each other in the short term.

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Assessing the Wet Deposition Mechanism of Benzo(a)pyrene in the Atmosphere by MF-DCCA

Atmosphere ◽

10.3390/atmos10060331 ◽

2019 ◽

Vol 10 (6) ◽

pp. 331 ◽

Cited By ~ 1

Author(s):

Chunqiong Liu ◽

Kai Shi ◽

Jian Liang ◽

Hongliang Huang

Keyword(s):

Time Scale ◽

Wet Deposition ◽

Cross Correlation ◽

Coefficient Of Determination ◽

Scaling Properties ◽

Temporal Scaling ◽

Cross Correlation Analysis ◽

Cross Correlations ◽

One Year

Based on the 19 year observation from 1998 to 2016 at the Tsuan Wan and Central/Western District monitoring stations in Hong Kong, the aim of this paper was to assess the wet deposition pathway of Benzo(a)pyrene (BaP) on a large time-scale. In order to achieve this goal, multi-fractal detrended cross-correlation analysis (MF-DCCA) was used to characterize the long-term cross-correlations behaviors and multi-fractal temporal scaling properties between BaP (or PM2.5) and precipitation. The results showed that the relationships between BaP and precipitation (or PM2.5) displayed long-term cross-correlation at the time-scale ranging from one month to one year; no cross-correlation between each other was observed in longer temporal scaling regimes (greater than one year). These results correspond to the atmospheric circulation of the Asian monsoon system and are explained in detail. Similar dynamic processes of the wet deposition of BaP and PM2.5 suggested that the main removal process of atmospheric BaP was rainfall deposits of PM2.5-bound BaP. Furthermore, cross-correlations between BaP (or PM2.5) and precipitation at the long time-scale have a multi-fractal nature and long-term persistent power-law decaying behavior. The temporal evolutions of the multi-fractality were investigated by the approach of a sliding window. Based on the evolution curves of multi-fractal parameters, the wet deposition pathway of PM2.5-bound BaP is discussed. Finally, the contribution degree of wet deposition to PM2.5-bound BaP was derived from the coefficient of determination. It was demonstrated that about 45% and 60% of atmospheric BaP removal can be attributed to the wet deposition pathway of PM2.5-bound BaP for the Tsuan Wan and Central/Western District areas, respectively. The findings in this paper are of great significance for further study on the removal mechanism of atmospheric BaP in the future. The MF-DCCA method provides a novel approach to assessing the geochemical cycle dynamics of BaP.

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Analyzing the Impact of COVID-19 on the Cross-Correlations between Financial Search Engine Data and Movie Box Office

Fluctuation and Noise Letters ◽

10.1142/s0219477521500218 ◽

2020 ◽

pp. 2150021

Author(s):

Renyu Wang ◽

Yujie Xie ◽

Hong Chen ◽

Guozhu Jia

Keyword(s):

Financial Market ◽

Cross Correlation ◽

Multifractal Spectrum ◽

Movie Industry ◽

Box Office ◽

Cross Correlation Analysis ◽

Cross Correlations ◽

The Cross ◽

Google Search ◽

The Impact

This paper explores the COVID-19 influences on the cross-correlation between the movie market and the financial market. The nonlinear cross-correlations between movie box office data and Google search volumes of financial terms such as Dow Jones Industrial Average (DJIA), NASDAQ and PMI are investigated based on multifractal detrended cross-correlation analysis (MF-DCCA). The empirical results show there are nonlinear cross-correlations between movie market and financial market. Metrics such as Hurst exponents, singular exponents and multifractal spectrum demonstrate that the cross-correlation between movie market and financial market is persistent, and the cross-correlation in long term is more stable than that in short term. In the COVID-19 period, the multifractal features of cross-correlation become stronger implying that COVID-19 enhanced the complexity between the movie industry and the financial market. Furthermore, through the rolling window analysis, the Hurst exponent dynamic trends indicate that COVID-19 has a clear influence on the cross-correlation between movie market and financial market.

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Time-Series and Dynamic Cross-Correlations Analysis on Unexpected Information: Evidence from Media News and Online Postings

Fluctuation and Noise Letters ◽

10.1142/s021947752050011x ◽

2019 ◽

Vol 19 (02) ◽

pp. 2050011

Author(s):

Yan Li ◽

Xiangyu Kong ◽

Xiao Li ◽

Zuochao Zhang

Keyword(s):

Mass Media ◽

New Media ◽

Trading Volume ◽

Cross Correlation ◽

Short Term ◽

Cross Correlations ◽

The Cross ◽

The One ◽

The Relationship

In this paper, we investigate the relationship between unexpected information from postings and news, and the unexpected information is measured by the residual of regressions of trading volume on numbers of news or postings. We mainly find that (i) There are significant positive contemporaneous correlations between the unexpected information coming from postings and different kinds of news; the correlation between the unexpected information coming from postings and new media news is stronger than that between the unexpected information coming from postings and mass media news; (ii) The unexpected information coming from postings could cause the unexpected information coming from news, but only the unexpected information coming from the mass media news could cause that coming from postings; (iii) There are persistent power-law cross-correlations between the unexpected information coming from postings and that coming from mass media news and new media news. The cross-correlation between the unexpected information coming from postings and new media news is more persistent than the one between the unexpected information coming from postings and mass media news. The cross-correlations are all more stable in long term than in short term. We attribute our findings above to the dissemination speed of the information on the Internet.

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

Fluctuation and Noise Letters ◽

10.1142/s0219477519500147 ◽

2019 ◽

Vol 18 (03) ◽

pp. 1950014 ◽

Cited By ~ 3

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.

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Neural Drive to Muscles in Stuttering

Journal of Speech Language and Hearing Research ◽

10.1044/jshr.3202.252 ◽

1989 ◽

Vol 32 (2) ◽

pp. 252-264 ◽

Cited By ~ 34

Author(s):

Anne Smith

Keyword(s):

Muscle Activity ◽

Muscle Activation ◽

Cross Correlation ◽

Frequency Range ◽

Neural Drive ◽

Activation Patterns ◽

Fluent Speech ◽

Muscle Activation Patterns ◽

Cross Correlation Analysis ◽

Cross Correlations

EMG recordings were made from muscles of the jaw, lip, and neck during speech of 10 stutterers and 10 nonstutterers. One-second records of disfluent behaviors of stutterers and of fluent speech of the normal speakers were analyzed by computing cross correlations between all possible muscle pairs and spectra for each muscle channel. The cross correlation analysis indicated that for both the disfluent behavior of stutterers and the fluent speech of nonstutterers, jaw muscles (including antagonistic pairs), lip muscles, and neck muscles tend to be coactivated. Thus, no dramatic differences in muscle activation patterns were revealed in the correlational analysis. In contrast, spectral analysis revealed differences between muscle activity during disfluent behavior and fluent speech. During disfluencies the muscles of 6 of the stutterers showed large, rhythmic oscillations in the frequency range of 5 to 12 Hz. Large oscillations were not observed in this frequency range in the muscle activity of normal speakers. The oscillations in muscle activity during disfluencies generally occurred at the same frequency in the various muscle systems studied. These results suggest that diverse muscles are subject to common oscillatory synaptic drive during disfluent behaviors and that this drive is disruptive to speech production. A reasonable speculation is that the disruptive oscillatory drive is produced by tremorogenic mechanisms.

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MULTISCALE DETRENDED CROSS-CORRELATION ANALYSIS OF STOCK MARKETS

Fractals ◽

10.1142/s0218348x14500078 ◽

2014 ◽

Vol 22 (04) ◽

pp. 1450007 ◽

Cited By ~ 8

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.

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MODELING CROSS-CORRELATIONS OF TRAFFIC FLOW

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127410027714 ◽

2010 ◽

Vol 20 (10) ◽

pp. 3323-3328 ◽

Cited By ~ 1

Author(s):

PENGJIAN SHANG ◽

KEQIANG DONG ◽

SANTI KAMAE

Keyword(s):

Time Series ◽

Traffic Flow ◽

Power Law ◽

Cross Correlation ◽

Heavy Tails ◽

Fluctuation Analysis ◽

Cross Correlation Analysis ◽

Cross Correlations ◽

Large Increment ◽

Active Research

The study of diverse natural and nonstationary signals has recently become an area of active research for physicists. This is because these signals exhibit interesting dynamical properties such as scale invariance, volatility correlation, heavy tails and fractality. The focus of the present paper is on the intriguing power-law autocorrelations and cross-correlations in traffic series. Detrended Cross-Correlation Analysis (DCCA) is used to study the traffic flow fluctuations. It is demonstrated that the time series, observed on the Anhua-Bridge highway in the Beijing Third Ring Road (BTRR), may exhibit power-law cross-correlations when they come from two adjacent sections or lanes. This indicates that a large increment in one traffic variable is more likely to be followed by large increment in the other traffic variable. However, for traffic time series derived from nonadjacent sections or lanes, we find that even though they are power-law autocorrelated, there is no cross-correlation between them with a unique exponent. Our results show that DCCA techniques based on Detrended Fluctuation Analysis (DFA) can be used to analyze and interpret the traffic flow.

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