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 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.

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.

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 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.

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.

scholarly journals The use of a Detrended Cross-Correlation Analysis on returns from agricultural commodities in the subprime crisis

2021 ◽  
Vol 16 (03) ◽  
pp. 119-137
Author(s):  
Luiza Lonardoni Paulino Schiavon ◽  
Antônio Fernando Crepaldi
Keyword(s):  
Financial Crisis ◽  
Correlation Analysis ◽  
Detrended Fluctuation Analysis ◽  
Cross Correlation ◽  
Fluctuation Analysis ◽  
Subprime Crisis ◽  
Product Portfolio ◽  
Agricultural Commodities ◽  
Cross Correlation Analysis ◽  
Detrended Fluctuation

Purpose: To understand the dynamics of the agricultural commodities market and predict a possible economic crisis, in addition to helping agricultural producers balance their product portfolio, diversifying their goods and reducing risks. Theoretical framework: Prices of agricultural commodities have changed significantly since 2002; although had been an increase in demand, where weather problems negatively affected supply, resulting in price increases. With the global financial crisis of 2008, there was a reduction in international credit and an increase in the US dollar against the Brazilian Real. Design/Methodology/Approach: Detrended Cross-Correlation Analysis and Detrended Fluctuation Analysis methods were used to understand the behavior of the cross correlations of the price of five Brazilian agribusiness commodities (cotton, sugar, coffee, corn and soybeans) for the previous periods, during and after the subprime crisis. Findings: Both methods showed a significant change in the behavior of the series in the period of crisis, when compared to their temporal neighborhoods. Research, Practical & Social Implications: It was found that the crisis changed the structure of the correlation of the returns on the commodities analyzed. This change implies alterations to a possible product portfolio in order to minimize risks. Originality/Value: The long-term nonlinear correlation behavior was calculated and analyzed on the temporal series for the return on the main agricultural commodities in the period of the subprime crisis and its temporal neighborhoods were calculated and analyzed, allowing several changes to be found in the product correlation structure, due to the crisis process. Keywords: Subprime Financial Crisis; Agricultural Commodities; Detrended Fluctuation Analysis; Detrended Cross-Correlation Analysis.

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.

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