scholarly journals Correlation Structures of PM2.5 Concentration Series in the Korean Peninsula

2019 ◽  
Vol 9 (24) ◽  
pp. 5441
Author(s):  
Gyuchang Lim ◽  
Seungsik Min
Keyword(s):  
Cross Correlation ◽  
Moving Average ◽  
Air Pollutant ◽  
Fluctuation Analysis ◽  
Auto Correlation ◽  
Cross Correlation Analysis ◽  
Correlation Structures ◽  
Spatio Temporal ◽  
Pm2.5 Concentration ◽  
Mass Concentrations

In this paper, the authors investigate the idiosyncratic features of auto- and cross-correlation structures of PM2.5 (particulate matter of diameter less than 2.5 μ m ) mass concentrations using DFA (detrended fluctuation analysis) methodologies. Since air pollutant mass concentrations are greatly affected by geographical, topographical, and meteorological conditions, their correlation structures can have non-universal properties. To this end, the authors firstly examine the spatio-temporal statistics of PM2.5 daily average concentrations collected from 18 monitoring stations in Korea, and then select five sites from those stations with overall lower and higher concentration levels in order to make up two groups, namely, G1 and G2, respectively. Firstly, to compare characteristic behaviors of the auto-correlation structures of the two groups, we performed DFA and MFDFA (multifractal DFA) analyses on both and then confirmed that the G2 group shows a clear crossover behavior in DFA and MFDFA analyses, while G1 shows no crossover. This finding implies that there are possibly two different scale-dependent underlying dynamics in G2. Furthermore, in order to confirm that different underlying dynamics govern G1 and G2, the authors conducted DCCA (detrended cross-correlation analysis) analysis on the same and different groups. As a result, in the same group, coupling behavior became more prominent between two series as the scale increased, while, in the different group, decoupling behavior was observed. This result also implies that different dynamics govern G1 and G2. Lastly, we presented a stochastic model, namely, ARFIMA (auto-regressive fractionally integrated moving average) with periodic trends, to reproduce behaviors of correlation structures from real PM2.5 concentration time series. Although those models succeeded in reproducing crossover behaviors in the auto-correlation structure, they yielded no valid results in decoupling behavior among heterogeneous groups.

Effect of Weather on Agricultural Futures Markets on the Basis of DCCA Cross-Correlation Coefficient Analysis

2016 ◽  
Vol 15 (02) ◽  
pp. 1650012 ◽  
Author(s):  
Guangxi Cao ◽  
Cuiting He ◽  
Wei Xu
Keyword(s):  
Correlation Analysis ◽  
Futures Markets ◽  
Cross Correlation ◽  
Moving Average ◽  
Correlation Coefficients ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Fluctuation Analysis ◽  
Cross Correlation Analysis ◽  
Agricultural Futures

This study investigates the correlation between weather and agricultural futures markets on the basis of detrended cross-correlation analysis (DCCA) cross-correlation coefficients and [Formula: see text]-dependent cross-correlation coefficients. In addition, detrended fluctuation analysis (DFA) is used to measure extreme weather and thus analyze further the effect of this condition on agricultural futures markets. Cross-correlation exists between weather and agricultural futures markets on certain time scales. There are some correlations between temperature and soybean return associated with medium amplitudes. Under extreme weather conditions, weather exerts different influences on different agricultural products; for instance, soybean return is greatly influenced by temperature, and weather variables exhibit no effect on corn return. Based on the detrending moving-average cross-correlation analysis (DMCA) coefficient and DFA regression results are similar to that of DCCA coefficient.

Traces of the Multifractal Nature of the Financial Crises in Turkey: Co-Movement of the Hölder Exponents and Large-Scale Forecast

2019 ◽  
Vol 19 (03) ◽  
pp. 2050029 ◽  
Author(s):  
Tunc Oygur ◽  
Gazanfer Unal
Keyword(s):  
Correlation Analysis ◽  
Financial Crises ◽  
Large Scale ◽  
Cross Correlation ◽  
Moving Average ◽  
Fluctuation Analysis ◽  
Real Sector ◽  
Cross Correlation Analysis ◽  
Hölder Exponents ◽  
Multifractal Behavior

This paper investigates the multifractal behavior of the probability of default (PD) of real sector firms and Turkey sovereign credit default swap (CDS). Moreover, we emphasize the co-movements of Hölder exponents during the financial crisis periods. For this reason, first, it is necessary to figure out the default probabilities of real sector firms. The default probability is evaluated weekly by the methodology of Moody’s Analytics, which is a commonly used approach, in which the market value of a firm is a call option written on its total assets. Multifractal detrended fluctuation analysis (MF-DFA), multifractal detrended cross-correlation analysis (MF-DCCA) and multifractal detrended moving average cross-correlation analysis (MF-X-DMA) techniques are applied to identify the multifractal behavior of the large-scale fluctuations of PDs and CDSs. In this way, we can evaluate the local Hurst exponents. Besides, the oscillation method is employed to estimate the pointwise and local Hölder exponents. In the period between January 2001 and March 2018, the structure of dynamic co-movements of Hölder exponents is determined by applying wavelet coherency methodology and the relations in crisis period are revealed. The selected period covers the crises with structural differences: Turkey banking crisis, the US sub-prime mortgage crisis and the European sovereign debt crisis that occurred in 2001, 2008 and 2009, respectively. Besides, during the periods of financial crises, among the local Hölder exponents, severely correlated large scales show multifractal features, and hence vector fractionally autoregressive integrated moving average (VFARIMA) forecasting provides better results than scalar models.

ECG classification using multifractal detrended moving average cross-correlation analysis

2021 ◽  
Author(s):  
Jian Wang ◽  
Wenjing Jiang ◽  
Yan Yan ◽  
Wenbing Chen ◽  
Junseok Kim
Keyword(s):  
Correlation Analysis ◽  
Cross Correlation ◽  
Signal Sequence ◽  
Moving Average ◽  
Subsequent Treatment ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Ecg Signals ◽  
Cross Correlation Analysis

Accurate detection of arrhythmia signal types is of great significance for the early detection of heart disease and its subsequent treatment. The primary purpose of this study is to explore an electrocardiogram (ECG) classification system to improve its performance and achieve excellent computing performance, especially for large sample datasets. We classified ECG signals using the Hurst exponent, which is an ECG feature extracted by multifractal detrended moving average cross-correlation analysis (MF-XDMA). In addition, we used multifractal methods such as multifractal detrended fluctuation analysis (MF-DFA), multifractal detrended cross-correlation analysis (MF-DCCA) and multifractal detrended moving average (MF-DMA) to extract the features of ECG signals, and we used a support vector machine (SVM) to classify the four types of feature data. The experimental results show that MF-XDMA-SVM has the best classification performance for atrial premature beat (APB) and bigeminy signals, which indicates that MF-XDMA-SVM is the most effective for the extraction of ECG signal sequence features among the four multifractal models.

scholarly journals Long-Range Behaviour and Correlation in DFA and DCCA Analysis of Cryptocurrencies

2019 ◽  
Vol 7 (3) ◽  
pp. 51 ◽  
Author(s):  
Natália Costa ◽  
César Silva ◽  
Paulo Ferreira
Keyword(s):  
Time Scales ◽  
Cross Correlation ◽  
Data Availability ◽  
Fluctuation Analysis ◽  
Financial Assets ◽  
Sliding Windows ◽  
Cross Correlation Analysis ◽  
Short Time ◽  
Detrended Fluctuation ◽  
Great Development

In recent years, increasing attention has been devoted to cryptocurrencies, owing to their great development and valorization. In this study, we propose to analyse four of the major cryptocurrencies, based on their market capitalization and data availability: Bitcoin, Ethereum, Ripple, and Litecoin. We apply detrended fluctuation analysis (the regular one and with a sliding windows approach) and detrended cross-correlation analysis and the respective correlation coefficient. We find that Bitcoin and Ripple seem to behave as efficient financial assets, while Ethereum and Litecoin present some evidence of persistence. When correlating Bitcoin with the other cryptocurrencies under analysis, we find that for short time scales, all the cryptocurrencies have statistically significant correlations with Bitcoin, although Ripple has the highest correlations. For higher time scales, Ripple is the only cryptocurrency with significant correlation.

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 Regional analysis of wind velocity patterns in complex terrain

Geofizika ◽  
2020 ◽  
Vol 36 (2) ◽  
pp. 111-130
Author(s):  
Radian Belu ◽  
Darko Koračin
Keyword(s):  
Statistical Analysis ◽  
Wind Energy ◽  
Wind Velocity ◽  
Time Scales ◽  
Complex Terrain ◽  
Spatial Scales ◽  
Fluctuation Analysis ◽  
Northern Nevada ◽  
Cross Correlation Analysis ◽  
Spatio Temporal

Wind energy is a weather and climate-dependent energy resource with natural spatio-temporal variabilities at time scales ranging from fraction of seconds to seasons and years, while at spatial scales it is strongly affected by the terrain and vegetation. To optimize wind energy systems and maximize the energy extraction, wind measurements on various time scales as well as wind energy forecasts are required and needed. This study focuses on spatio-temporal characteristics of the wind velocity in complex terrain, relevant to wind energy assessment, operation, and grid integration, using data collected at 11 towers ranging from 40 to 80 m tall over a 12-year period in complex terrain of western-central and northern Nevada, USA. The autocorrelation analysis, Detrended Fluctuation Analysis (DFA) and Detrended Cross-Correlation Analysis (DCCA) showed strong coherence between the wind speed and direction with slowly decreasing amplitude of the multi-day periodicity with increasing lag periods. Besides pronounced diurnal periodicity at all locations, statistical analysis and DFA also showed significant seasonal and annual periodicities, long-memory persistence with similar characteristics at all sites and towers with a relatively narrow range of the Weibull parameters. The DCCA indicates similar wind patterns at each tower, and strong correlations between measurement sites in spite of separations of about 300 km across the towers’ setup. The northern Nevada area exhibits higher wind resource potential and higher wind persis-tence compared to the western-central region. Overall, the DFA and DCCA results suggest higher degree of complementarity among wind data at measure-ment sites compared to previous standard statistical analysis.

scholarly journals CORRELAÇÃO DE LONGO ALCANCE TEMPORAL DA VELOCIDADE DO VENTO NOS MUNICÍPIOS DE CEARÁ-MIRIM E NATAL NO RIO GRANDE DO NORTE

2017 ◽  
Vol 8 ◽  
pp. 56 ◽  
Author(s):  
Neilson Ferreira de Lima ◽  
Marcos Antônio Chaves Freire ◽  
Josimar José dos Santos ◽  
Rodrigo Ricardo Cavalcante de Albuquerque
Keyword(s):  
Correlation Analysis ◽  
Detrended Fluctuation Analysis ◽  
Cross Correlation ◽  
Rio Grande ◽  
Fluctuation Analysis ◽  
Cross Correlation Analysis ◽  
Detrended Fluctuation

A energia eólica é uma fonte natural de energia renovável e utilizada em diversos países para o abastecimento energético de residências, fábricas e empresas. Para os países que possuem hidrelétricas como a principal fonte geradora de energia, como o Brasil, por exemplo, a energia eólica é muito importante, porque ela não consome água, é renovável, limpa e não causa danos ambientais como outras fontes energéticas poluentes e sujas. Diversos estudos são realizados a fim de observar o comportamento do vento, em particular às correlações com outras variáveis como radiação solar, temperatura máxima ou mínima e umidade relativa do ar. Para fazerem inferência das observações do vento pesquisadores tem empregado diversas ferramentas estatísticas como médias móveis, média móvel ponderada e suavização exponencial. Nosso interesse é verificar as correlações de curto ou longo alcance persistente/antipersistente em séries temporais de ventos dos municípios Natal e Ceará-Mirim. Para realizar o estudo da correlação do vento se aplicou os métodos estatísticos denominados Detrended Fluctuation Analysis (DFA) e Detrended Cross-Correlation Analysis –DCCA, isto é análise da flutuação sem tendências e análise da correlação cruzada sem tendências. Nesta pesquisa observou-se que a série temporal do vento tem uma forte correlação de longo alcance persistente, significando que valores altos de velocidade do vento tem maior probabilidade de ser seguido por valores altos; e, valores baixos na velocidade do vento tem maior probabilidade de ser seguido por valores baixos.

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