scholarly journals Multifractal Cross Correlation Analysis of Agro-Meteorological Datasets (Including Reference Evapotranspiration) of California, United States

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1116
Author(s):  
Adarsh Sankaran ◽  
Jaromir Krzyszczak ◽  
Piotr Baranowski ◽  
Archana Devarajan Sindhu ◽  
Nandhineekrishna Kumar ◽  
...  
Keyword(s):  
Time Series ◽  
Cross Correlation ◽  
Reference Evapotranspiration ◽  
Multifractal Spectrum ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Scaling Exponents ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Cross Correlation Analysis ◽  
The Individual

The multifractal properties of six acknowledged agro-meteorological parameters, such as reference evapotranspiration (ET0), wind speed (U), incoming solar radiation (SR), air temperature (T), air pressure (P), and relative air humidity (RH) of five stations in California, USA were examined. The investigation of multifractality of datasets from stations with differing terrain conditions using the Multifractal Detrended Fluctuation Analysis (MFDFA) showed the existence of a long-term persistence and multifractality irrespective of the location. The scaling exponents of SR and T time series are found to be higher for stations with higher altitudes. Subsequently, this study proposed using the novel multifractal cross correlation (MFCCA) method to examine the multiscale-multifractal correlations properties between ET0 and other investigated variables. The MFCCA could successfully capture the scale dependent association of different variables and the dynamics in the nature of their associations from weekly to inter-annual time scales. The multifractal exponents of P and U are consistently lower than the exponents of ET0, irrespective of station location. This study found that joint scaling exponent was nearly the average of scaling exponents of individual series in different pairs of variables. Additionally, the α-values of joint multifractal spectrum were lower than the α values of both of the individual spectra, validating two universal properties in the MFCCA studies for agro-meteorological time series. The temporal evolution of cross-correlation determined by the MFCCA successfully captured the dynamics in the nature of associations in the P-ET0 link.

scholarly journals Multifractal Cross Correlation Analysis of Agro-meteorological Datasets (Including Reference Evapotranspiration) of California, United States

2020 ◽  
Author(s):  
Adarsh Sankaran ◽  
Jaromir Krzyszczak ◽  
Piotr Baranowski ◽  
Archana Devarajan Sindhu ◽  
Nandhinee Krishna Pradeep ◽  
...  
Keyword(s):  
Time Series ◽  
Los Angeles ◽  
Cross Correlation ◽  
Reference Evapotranspiration ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Air Humidity ◽  
Scaling Exponents ◽  
Relative Air Humidity ◽  
Cross Correlation Analysis

This paper examined the multifractal properties of six acknowledged agro-meteorological parameters, such as reference evapotranspiration (ET0), wind speed (U), incoming solar radiation (SR), air temperature (T), air pressure (P), and relative air humidity (RH) of five stations in California, USA. The investigation of multifractality of datasets from stations with differing terrain conditions: Dagget, Bakersfield, Santa Maria, Los Angeles and San Diego using the Multifractal Detrended Fluctuation Analysis showed the existence of a long term persistence and multifractality irrespective of the location. The scaling exponents of SR and ET0 time series are found to be higher for stations with higher altitudes. Subsequently, this study proposed using the novel multifractal cross correlation (MFCCA) method to examine the multiscale-multifractal correlations properties between ET0 and other investigated variables. MFCCA could successfully capture the scale dependent association of different variables and the dynamics in the nature of their associations from seasonal to multi-annual time scale. The multifractal exponents of pressure and relative air humidity are consistently lower than the exponents of ET0, irrespective of station location. This study found that joint scaling exponent was nearly the average of scaling exponents of individual series in different pairs of variables. Additionally, the α-values of joint multifractal spectrum were lower than the α values of both of the individual spectra, validating two universal properties in the mutifractal cross correlation studies for agro-meteorological time series. The temporal evolution of cross-correlation showed similar pattern for all pair-wise associations involving ET0, except for the RH-ET0 link.

scholarly journals Multifractal Analysis of Market Efficiency across Structural Breaks: Implications for the Adaptive Market Hypothesis

2020 ◽  
Vol 13 (10) ◽  
pp. 248
Author(s):  
Ashok Chanabasangouda Patil ◽  
Shailesh Rastogi
Keyword(s):  
Long Memory ◽  
Cross Correlation ◽  
Structural Breaks ◽  
Primary Objective ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Price Series ◽  
Cross Correlation Analysis ◽  
Large Fluctuations ◽  
Secondary Objective

The primary objective of this paper is to assess the behavior of long memory in price, volume, and price-volume cross-correlation series across structural breaks. The secondary objective is to find the appropriate structural breaks in the price series. The structural breaks in the series are identified using the Bai and Perron procedure, and in each segment, Multifractal Detrended Fluctuation Analysis (MFDFA) and Multifractal Detrended Cross-Correlation Analysis (MFDCCA) are conducted to capture the long memory in each series. The price series is persistent in small fluctuations and anti-persistent in large fluctuations across all the structural segments. This confirms that long memory in the series is not affected by the structural breaks. Both volume and price-volume cross-correlation are anti-persistent in all the structural segments. In other words, volume acts as a carrier of the information only in the non-volatile (normal) market. The varying Hurst exponent across the structural segments indicates the varying levels of persistence and signifies the volatile market. The findings of the study are useful for understanding the practical implications of the Adaptive Market Hypothesis (AMH).

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

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
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.

MODELING CROSS-CORRELATIONS OF TRAFFIC FLOW

2010 ◽  
Vol 20 (10) ◽  
pp. 3323-3328 ◽  
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.

scholarly journals Fluctuation Characteristics of Water Level and Water Temperature of Huize Well Based on MF-DCCA

2021 ◽  
Vol 39 (3) ◽  
pp. 825-832
Author(s):  
Jian Yu ◽  
Lili Sui ◽  
Yirong Xu ◽  
Baoming Chi
Keyword(s):  
Time Series ◽  
Water Temperature ◽  
Water Level ◽  
Cross Correlation ◽  
Correlation Method ◽  
Multifractal Spectrum ◽  
Earthquake Precursor ◽  
Observation Data ◽  
Cross Correlation Analysis ◽  
Abnormal Increase

In recent decades, the network of seismic subsurface fluid observatories is developing constantly, the observation data of subsurface fluids are enriched accordingly, which provides a favorable condition for the research on the formation, occurrence, and development of earthquakes. In the observation data of subsurface fluids, water level and water temperature changes are very important observation indicators, and their fluctuation sequences are quite complicated. Therefore, this paper employed a non-linear cross-correlation method to study the relationship between the water level and water temperature of Huize Well from 2004 to 2006, and found that there’s a significant cross-correlation between the time series of water level and water temperature; then, this study adopted DCCA (detrended cross-correlation analysis) to calculate the cross-correlation coefficient under different scales and explore the continuous changes of water level and water temperature; at last, this paper used the MF-DCCA (Multifractal-DCCA) method to prove that there’s multifractal cross-correlation between the time series of water level and water temperature. Before the M5.1 earthquake in Huize area, there’s an abnormal increase in the width of the multifractal spectrum of the water level and water temperature drawn with a sliding window of 500-hour, and this is a possible earthquake precursor.

scholarly journals Investigation of the Implications of “Haze Special Law” on Air Quality in South Korea

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Jian Wang ◽  
Junseok Kim ◽  
Wei Shao
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
South Korea ◽  
Pollution Index ◽  
Multifractal Spectrum ◽  
Fluctuation Analysis ◽  
Air Pollution Control ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Before And After ◽  
The Law

In this work, the air pollution index in three cities (Seoul, Busan, and Daegu) in South Korea was studied using multifractal detrended fluctuation analysis (MF-DFA). Hurst, Renyi, and Holder exponents were used to analyze the characteristics of the concentration time series of PM2.5 and NO2. The results showed that multifractality exists in each season interval and the multifractal degree of PM2.5 is stronger than that of NO2. To investigate the effects of the implementation of the “haze special law” on February 15, 2019, we analyzed the time series of PM2.5 during the time periods from February 15, 2018, to December 16, 2018, and February 15, 2019, to December 16, 2019. We found that the multifractal spectrum width after the implementation of the law was narrower than that before the law for all the cities, which shows that the enactment of the law has played a role in improving the efficiency of air pollution control in South Korea. We also conclude that the major effects of the law will be particularly visible in larger cities. To study the main causes of multifractality, the shuffled and phase-randomized series were analyzed using MF-DFA, and the results demonstrated that the fat-tailed distribution resulted in the multifractality of the time series before and after the implementation of the “haze special law” in Seoul and Daegu, whereas long-range correlation resulted in multifractality of the series before and after the implementation of the law in Busan.

scholarly journals Distribution of Air Temperature Multifractal Characteristics Over Greece

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 45 ◽  
Author(s):  
Nikolaos Kalamaras ◽  
Chris Tzanis ◽  
Despina Deligiorgi ◽  
Kostas Philippopoulos ◽  
Ioannis Koutsogiannis
Keyword(s):  
Time Series ◽  
Multifractal Spectrum ◽  
Weather Conditions ◽  
Fluctuation Analysis ◽  
Spatial Distributions ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Natural Processes ◽  
Complex Interactions ◽  
Local Fluctuations ◽  
Almost All

In this study, Multifractal Detrended Fluctuation Analysis (MF-DFA) is applied to daily temperature time series (mean, maximum and minimum values) from 22 Greek meteorological stations with the purpose of examining firstly their scaling behavior and then checking if there are any differences in their multifractal characteristics. The results showed that the behavior is the same at almost all stations, i.e., time series are positive long-term correlated and their multifractal structure is insensitive to local fluctuations with large magnitude. Moreover, this study deals with the spatial distribution of the main characteristics of multifractal (singularity) spectrum: the dominant Hurst exponent, the width of the spectrum, the asymmetry and the truncation type of the spectrum. The spatial distributions are discussed in terms of possible effects from various climatic features. In general, local atmospheric circulation and weather conditions are found to affect the shape of the spectrum and the corresponding spatial distributions. Furthermore, the intercorrelation of the main multifractal spectrum parameters resulted in a well-defined group of stations sharing similar multifractal characteristics. The results indicate the usefulness of the non-linear analysis in climate research due to the complex interactions among the natural processes.

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.

scholarly journals Multifractal Detrended Cross-Correlation Analysis of Electricity and Carbon Markets in China

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Shaohui Zou ◽  
Tian Zhang
Keyword(s):  
Correlation Analysis ◽  
Cross Correlation ◽  
Carbon Price ◽  
Research Area ◽  
Carbon Market ◽  
Fluctuation Analysis ◽  
Carbon Markets ◽  
Strong Negative Correlation ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Cross Correlation Analysis

With the development of carbon market, the complex dynamic relationship between electricity and carbon market has become the focus of energy research area. In this paper, we applied a new developed multifractal detrended cross-correlation analysis method to investigate the cross-correlation and multifractality between electricity and carbon markets. We analyze the daily return of electricity and carbon prices over a period of 6 years to do the research. The results show that, firstly, we find that there is a strong negative correlation between domestic carbon price and electricity price and a significant cross-correlation between the return series of electricity and carbon markets. Secondly, through multifractal detrended fluctuation analysis, it is proven that there are obvious multifractal characteristics in the return series of electricity and carbon markets, and the results of traditional linear analysis are unreliable. We also find that, based on multifractal detrended cross-correlation analysis, the law cross-correlation between electricity and carbon markets exists significantly. The long-range correlation of small fluctuations and large fluctuations and the fat tail distribution of return series are the reasons for the formation of multifractality.

scholarly journals Multifractality Nature of Microtubule Dynamic Instability Process

2020 ◽  
Author(s):  
Vahid Rezania ◽  
Ferry C. Sudirga ◽  
Jack A. Tuszynski
Keyword(s):  
Experimental Data ◽  
Time Series ◽  
Dynamic Instability ◽  
Simulated Data ◽  
Multifractal Spectrum ◽  
Mathematical Representation ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Synthetic Time Series ◽  
First Time

AbstractThe irregularity of growing and shortening patterns observed experimentally in microtubules reflects a dynamical system that fluctuates stochastically between assembly and disassembly phases. The observed time series of microtubule lengths have been extensively analyzed to shed light on structural and dynamical properties of microtubules. Here, for the first time, Multifractal Detrended Fluctuation analysis (MFDFA) has been employed to investigate the multifractal and topological properties of both experimental and simulated microtubule time series. We find that the time dependence of microtubule length possesses true multifractal characteristics and cannot be described by monofractal distributions. Based on the multifractal spectrum profile, a set of multifractal indices have been calculated that can be related to the level of dynamical activities of microtubules. We also show that the resulting multifractal spectra for the simulated data might not be comparable with experimental data.Statement of SignificanceMicrotubules are some of the most important subcellular structures involved in a multitude of functions in all eukaryotic cells. In addition to their cylindrical geometry, their polymerization/depolymerization dynamics, termed dynamic instability, is unique among all protein polymers. In this paper we demonstrate that there is a very specific mathematical representation of microtubule growth and shrinkage time series in terms of multifractality. We further show that using this characteristic, one can distinguish real experimental data from synthetic time series generated from computer simulations.

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