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Journal articles on the topic 'Detrended cross correlation analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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1

Wang, Jun, and Da-Qing Zhao. "Detrended cross-correlation analysis of electroencephalogram." Chinese Physics B 21, no. 2 (February 2012): 028703. http://dx.doi.org/10.1088/1674-1056/21/2/028703.

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2

YIN, YI, and PENGJIAN SHANG. "MULTISCALE DETRENDED CROSS-CORRELATION ANALYSIS OF STOCK MARKETS." Fractals 22, no. 04 (November 12, 2014): 1450007. http://dx.doi.org/10.1142/s0218348x14500078.

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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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3

MAO, XUEGENG, and PENGJIAN SHANG. "DETRENDED CROSS-CORRELATION ANALYSIS BETWEEN MULTIVARIATE TIME SERIES." Fractals 26, no. 04 (August 2018): 1850058. http://dx.doi.org/10.1142/s0218348x18500585.

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It is a crucial topic to identify the cross-correlations between time series in multivariate systems. In this paper, we extend the detrended cross-correlation analysis (DCCA) into the multivariate systems, assigned multivariate detrended cross-correlation analysis (MVDCCA). Numerical simulations of synthetic multivariate time series generated by two-exponent and mix-correlated ARFIMA processes are applied to illustrate the validity of the proposed MVDCCA. Results show that the external coupling parameter determines the strength of cross-correlation no matter that it is inter-independent or correlated among channels in a certain multivariate time series. The MVDCCA method is robust enough to detect the scale properties of time series by estimating the Hurst exponent. And we use cross-correlation coefficient to quantify the level of cross-correlations clearly. Furthermore, the MVDCCA method performs well when applied to the stock markets combining the stock daily price returns and trading volume of stock indices. By comparing results only using stock daily price returns in published literatures, we find that the higher recognizability between the pair stock indices can be observed whatever from the same regions or different regions in multivariate situations and the conclusions are more comprehensive.
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4

Zhao, Jun Chang, Wan Hu Dou, Hong Da Ji, and Jun Wang. "Detrended Cross-Correlation Analysis of Epilepsy Electroencephalagram Signals." Advanced Materials Research 765-767 (September 2013): 2664–67. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2664.

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The cross-correlation performance between epilepsy electroencephalogram (EEG) signals reflects the status of epilepsy patients which has importance for analyzing long-range correlation of non-stationary signals. For the first time, detrended cross-correlation analysis (DCCA) was applied to analyze different physiological and pathological states of epilepsy EEG signals. It were compared the difference of DCCA values between epilepsy patients EEG signals and normal subjects EEG signals. It was found that the DCCA values of epilepsy patients EEG signals increased compared the normal subjects EEG signals which can be helpful for medical diagnosis and treatment.
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5

Roume, C., Z. M. H. Almurad, M. Scotti, S. Ezzina, H. Blain, and D. Delignières. "Windowed detrended cross-correlation analysis of synchronization processes." Physica A: Statistical Mechanics and its Applications 503 (August 2018): 1131–50. http://dx.doi.org/10.1016/j.physa.2018.08.074.

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6

Marinho, E. B. S., A. M. Y. R. Sousa, and R. F. S. Andrade. "Using Detrended Cross-Correlation Analysis in geophysical data." Physica A: Statistical Mechanics and its Applications 392, no. 9 (May 2013): 2195–201. http://dx.doi.org/10.1016/j.physa.2012.12.038.

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7

Wang, Fang, Gui-ping Liao, Xiao-yang Zhou, and Wen Shi. "Multifractal detrended cross-correlation analysis for power markets." Nonlinear Dynamics 72, no. 1-2 (January 3, 2013): 353–63. http://dx.doi.org/10.1007/s11071-012-0718-2.

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8

Dong, Keqiang, and Xiaojie Gao. "Higher-Order Multifractal Detrended Partial Cross-Correlation Analysis for the Correlation Estimator." Complexity 2020 (June 4, 2020): 1–10. http://dx.doi.org/10.1155/2020/7495058.

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In this paper, we develop a new method to measure the nonlinear interactions between nonstationary time series based on the detrended cross-correlation coefficient analysis. We describe how a nonlinear interaction may be obtained by eliminating the influence of other variables on two simultaneous time series. By applying two artificially generated signals, we show that the new method is working reliably for determining the cross-correlation behavior of two signals. We also illustrate the application of this method in finance and aeroengine systems. These analyses suggest that the proposed measure, derived from the detrended cross-correlation coefficient analysis, may be used to remove the influence of other variables on the cross-correlation between two simultaneous time series.
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9

Li, Wan, Zhu Yongqian, Deng Xiaocheng, and Lin Jiaoxiu. "Multifractal Detrended Cross-Correlation Analysis of Geochemical Element Concentration." Open Materials Science Journal 8, no. 1 (December 31, 2014): 136–40. http://dx.doi.org/10.2174/1874088x01408010136.

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We use multifractal detrended cross -fluctuation analysis (MF-DXA) to investigate nonlinear behavior of geochemical element concentration, Au-Cu-Pb-Zn-Ag, in Shangzhuang Deposit, Shandong Province, China. We find that the generalized Hurst exponent h(q) and cross-correlation exponent hxy(q) decrease with the increase of q, which indicate that all element concentration series and their cross pairs exhibit multifractal phenomena. By comparing the variability of h(q) and hxy(q), we have found that the multifractal behavior is more obvious when q > 0 than q < 0 for the element Au- Cu-Pb-Zn and their cross pairs. These analyses, given quantitative information about the complexity of the element concentration, lead to a better understanding of the geochemical phenomena underlying mineralization process.
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10

Zhao, Longfeng, Wei Li, Andrea Fenu, Boris Podobnik, Yougui Wang, and H. Eugene Stanley. "Theq-dependent detrended cross-correlation analysis of stock market." Journal of Statistical Mechanics: Theory and Experiment 2018, no. 2 (February 14, 2018): 023402. http://dx.doi.org/10.1088/1742-5468/aa9db0.

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11

K., Hema Sri Sai, Mayukha Pal, and Manimaran P. "Multifractal detrended partial cross-correlation analysis on Asian markets." Physica A: Statistical Mechanics and its Applications 531 (October 2019): 121778. http://dx.doi.org/10.1016/j.physa.2019.121778.

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12

Zhao, Xiaojun, Pengjian Shang, Aijing Lin, and Gang Chen. "Multifractal Fourier detrended cross-correlation analysis of traffic signals." Physica A: Statistical Mechanics and its Applications 390, no. 21-22 (October 2011): 3670–78. http://dx.doi.org/10.1016/j.physa.2011.06.018.

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13

El Alaoui, Marwane, and Saâd Benbachir. "Multifractal detrended cross-correlation analysis in the MENA area." Physica A: Statistical Mechanics and its Applications 392, no. 23 (December 2013): 5985–93. http://dx.doi.org/10.1016/j.physa.2013.08.002.

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14

Yin, Yi, and Pengjian Shang. "Multiscale multifractal detrended cross-correlation analysis of traffic flow." Nonlinear Dynamics 81, no. 3 (April 8, 2015): 1329–47. http://dx.doi.org/10.1007/s11071-015-2072-7.

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15

He, Ling-Yun, and Shu-Peng Chen. "Multifractal Detrended Cross-Correlation Analysis of agricultural futures markets." Chaos, Solitons & Fractals 44, no. 6 (June 2011): 355–61. http://dx.doi.org/10.1016/j.chaos.2010.11.005.

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16

Cao, Guangxi, Cuiting He, and Wei Xu. "Effect of Weather on Agricultural Futures Markets on the Basis of DCCA Cross-Correlation Coefficient Analysis." Fluctuation and Noise Letters 15, no. 02 (June 2016): 1650012. http://dx.doi.org/10.1142/s0219477516500127.

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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.
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17

Cao, Guangxi, Jie Cao, Longbing Xu, and LingYun He. "Detrended cross-correlation analysis approach for assessing asymmetric multifractal detrended cross-correlations and their application to the Chinese financial market." Physica A: Statistical Mechanics and its Applications 393 (January 2014): 460–69. http://dx.doi.org/10.1016/j.physa.2013.08.074.

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18

KONG, QINGGE, QING YU, MEIFENG DAI, YUE ZONG, XIAODONG WANG, JIANZHONG JIANG, and HUOJUN RUAN. "MULTIFRACTAL DETRENDED FLUCTUATION ANALYSIS BASED ON PSEUDO-BILINEAR FRACTAL INTERPOLATION FUNCTIONS ON METALLIC GLASSES." Fractals 26, no. 04 (August 2018): 1850047. http://dx.doi.org/10.1142/s0218348x18500470.

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Based on the multifractal detrended cross-correlation analysis, which is the most effective way to detect long-range cross-correlation of time series, in this paper, we present a new method called multifractal detrended fluctuation analysis based on pseudo-bilinear fractal interpolation functions (MFDFA-PBFIF). In order to get a better detrended effect, we replace the polynomial fitting with PBFIFs in detrended process, and the result shows that the MFDFA-PBFIF can achieve a more accurate result. Then, we analyze the Legendre spectrum to detect the multifractal property on metallic glasses with MFDFA-PBFIF.
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19

Tzanis, Chris G., Ioannis Koutsogiannis, Kostas Philippopoulos, and Nikolaos Kalamaras. "Multifractal Detrended Cross-Correlation Analysis of Global Methane and Temperature." Remote Sensing 12, no. 3 (February 7, 2020): 557. http://dx.doi.org/10.3390/rs12030557.

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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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20

Costa, Natália, César Silva, and Paulo Ferreira. "Long-Range Behaviour and Correlation in DFA and DCCA Analysis of Cryptocurrencies." International Journal of Financial Studies 7, no. 3 (September 15, 2019): 51. http://dx.doi.org/10.3390/ijfs7030051.

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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.
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21

LIN, AIJING, and PENGJIAN SHANG. "MINIMIZING PERIODIC TRENDS BY APPLYING LAPLACE TRANSFORM." Fractals 19, no. 02 (June 2011): 203–11. http://dx.doi.org/10.1142/s0218348x11005245.

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Rescaled range analysis (R/S analysis), detrended fluctuation analysis (DFA) and detrended moving average (DMA) are widely-used methods for detection of long-range correlations in time series. Detrended cross-correlation analysis (DCCA) is a recently developed method to quantify the cross-correlations of two non-stationary time series. Another method for studying auto-correlations and cross-correlations was presented by Sergio Arianos and Anna Carbone in 2009. Recent studies have reported the susceptibility of this methods to periodic trends, which can result in spurious crossovers. In this paper, we propose the modified methods base on Laplace transform to minimizing the effect of periodic trends. The effectiveness of our techniques are demonstrated on stock data corrupted with periodic trends.
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22

Lin, Tzu-Kang, and Haikal Fajri. "Damage detection of structures with detrended fluctuation and detrended cross-correlation analyses." Smart Materials and Structures 26, no. 3 (February 7, 2017): 035027. http://dx.doi.org/10.1088/1361-665x/aa59d7.

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23

Wang, Fang, Jian Xu, and Qingju Fan. "Statistical properties of the detrended multiple cross-correlation coefficient." Communications in Nonlinear Science and Numerical Simulation 99 (August 2021): 105781. http://dx.doi.org/10.1016/j.cnsns.2021.105781.

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24

Wei, Xiaowei, Hongbo Zhang, Xinghui Gong, Xingchen Wei, Chiheng Dang, and Tong Zhi. "Intrinsic Cross-Correlation Analysis of Hydro-Meteorological Data in the Loess Plateau, China." International Journal of Environmental Research and Public Health 17, no. 7 (April 2, 2020): 2410. http://dx.doi.org/10.3390/ijerph17072410.

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The purpose of this study is to illustrate intrinsic correlations and their temporal evolution between hydro-meteorological elements by building three-element-composed system, including precipitation (P), runoff (R), air temperature (T), evaporation (pan evaporation, E), and sunshine duration (SD) in the Wuding River Basin (WRB) in Loess Plateau, China, and to provide regional experience to correlational research of global hydro-meteorological data. In analysis, detrended partial cross-correlation analysis (DPCCA) and temporal evolution of detrended partial-cross-correlation analysis (TDPCCA) were employed to demonstrate the intrinsic correlation, and detrended cross-correlation analysis (DCCA) coefficient was used as comparative method to serve for performance tests of DPCCA. In addition, a novel way was proposed to estimate the contribution of a variable to the change of correlation between other two variables, namely impact assessment of correlation change (IACC). The analysis results in the WRB indicated that (1) DPCCA can analyze the intrinsic correlations between two hydro-meteorological elements by removing potential influences of the relevant third one in a complex system, providing insights on interaction mechanisms among elements under changing environment; (2) the interaction among P, R, and E was most strong in all three-element-composed systems. In elements, there was an intrinsic and stable correlation between P and R, as well as E and T, not depending on time scales, while there were significant correlations on local time scales between other elements, i.e., P-E, R-E, P-T, P-SD, and E-SD, showing the correlation changed with time-scales; (3) TDPCCA drew and highlighted the intrinsic correlations at different time-scales and its dynamics characteristic between any two elements in the P-R-E system. The results of TDPCCA in the P-R-E system also demonstrate the nonstationary correlation and may give some experience for improving the data quality. When establishing a hydrological model, it is suitable to only use P, R, and E time series with significant intrinsic correlation for calibrating model. The IACC results showed that taking pan evaporation as the representation of climate change (barring P), the impacts of climate change on the non-stationary correlation of P and R was estimated quantitatively, illustrating the contribution of climate to the correlation variation was 30.9%, and that of underlying surface and direct human impact accounted for 69.1%.
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25

Zhang, Wei, Pengfei Wang, Xiao Li, and Dehua Shen. "Multifractal Detrended Cross-Correlation Analysis of the Return-Volume Relationship of Bitcoin Market." Complexity 2018 (July 26, 2018): 1–20. http://dx.doi.org/10.1155/2018/8691420.

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We investigate the cross-correlations of return-volume relationship of the Bitcoin market. In particular, we select eight exchange rates whose trading volume accounts for more than 98% market shares to synthesize Bitcoin indexes. The empirical results based on multifractal detrended cross-correlation analysis (MF-DCCA) reveal that (1) the nonlinear dependencies and power-law cross-correlations in return-volume relationship are found; (2) all cross-correlations are multifractal, and there are antipersistent behaviors of cross-correlation for q=2; (3) the price of small fluctuations is more persistent than that of the volume, while the volume of larger fluctuations is more antipersistent; and (4) the rolling window method shows that the cross-correlations of return-volume are antipersistent in the entire sample period.
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26

WANG, JING, PENGJIAN SHANG, and WEIJIE GE. "MULTIFRACTAL CROSS-CORRELATION ANALYSIS BASED ON STATISTICAL MOMENTS." Fractals 20, no. 03n04 (September 2012): 271–79. http://dx.doi.org/10.1142/s0218348x12500259.

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We introduce a new method, multifractal cross-correlation analysis based on statistical moments (MFSMXA), to investigate the long-term cross-correlations and cross-multifractality between time series generated from complex system. Efficiency of this method is shown on multifractal series, comparing with the well-known multifractal detrended cross-correlation analysis (MFXDFA) and multifractal detrending moving average cross-correlation analysis (MFXDMA). We further apply this method on volatility time series of DJIA and NASDAQ indices, and find some interesting results. The MFSMXA has comparative performance with MFXDMA and sometimes perform slightly better than MFXDFA. Multifractal nature exists in volatility series. In addition, we find that the cross-multifractality of volatility series is mainly due to their cross-correlations, via comparing the MFSMXA results for original series with those for shuffled series.
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27

Kang, Deok Du, Dong In Lee, Kyungsik Kim, Gyuchang Lim, and Deok-Ho Ha. "Dynamical mechanism in meteorological factors using detrended cross-correlation analysis." Journal of the Korean Physical Society 65, no. 5 (September 2014): 577–90. http://dx.doi.org/10.3938/jkps.65.577.

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28

Zhai, Lu-Sheng, and Ruo-Yu Liu. "Local detrended cross-correlation analysis for non-stationary time series." Physica A: Statistical Mechanics and its Applications 513 (January 2019): 222–33. http://dx.doi.org/10.1016/j.physa.2018.09.006.

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29

Contreras-Reyes, Javier E., and Byron J. Idrovo-Aguirre. "Backcasting and forecasting time series using detrended cross-correlation analysis." Physica A: Statistical Mechanics and its Applications 560 (December 2020): 125109. http://dx.doi.org/10.1016/j.physa.2020.125109.

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30

Shi, Wenbin, Pengjian Shang, Jing Wang, and Aijing Lin. "Multiscale multifractal detrended cross-correlation analysis of financial time series." Physica A: Statistical Mechanics and its Applications 403 (June 2014): 35–44. http://dx.doi.org/10.1016/j.physa.2014.02.023.

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31

Dutta, Srimonti, Dipak Ghosh, and Shukla Samanta. "Multifractal detrended cross-correlation analysis of gold price and SENSEX." Physica A: Statistical Mechanics and its Applications 413 (November 2014): 195–204. http://dx.doi.org/10.1016/j.physa.2014.06.081.

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32

Zhang, Chen, Zhiwei Ni, and Liping Ni. "Multifractal detrended cross-correlation analysis between PM2.5 and meteorological factors." Physica A: Statistical Mechanics and its Applications 438 (November 2015): 114–23. http://dx.doi.org/10.1016/j.physa.2015.06.039.

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33

Yin, Yi, and Pengjian Shang. "Asymmetric multiscale detrended cross-correlation analysis of financial time series." Chaos: An Interdisciplinary Journal of Nonlinear Science 24, no. 3 (September 2014): 032101. http://dx.doi.org/10.1063/1.4893442.

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34

Balocchi, R., M. Varanini, and A. Macerata. "Quantifying different degrees of coupling in detrended cross-correlation analysis." EPL (Europhysics Letters) 101, no. 2 (January 1, 2013): 20011. http://dx.doi.org/10.1209/0295-5075/101/20011.

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35

Hirekhan, Sunil R., and Ramchandra R. Manthalkar. "The detrended fluctuation and cross-correlation analysis of EEG signals." International Journal of Intelligent Systems Design and Computing 2, no. 2 (2018): 139. http://dx.doi.org/10.1504/ijisdc.2018.096330.

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Manthalkar, Ramchandra R., and Sunil R. Hirekhan. "The detrended fluctuation and cross-correlation analysis of EEG signals." International Journal of Intelligent Systems Design and Computing 2, no. 2 (2018): 139. http://dx.doi.org/10.1504/ijisdc.2018.10017634.

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37

Xue, Chongfeng, Pengjian Shang, and Wang Jing. "Multifractal Detrended Cross-Correlation Analysis of BVP model time series." Nonlinear Dynamics 69, no. 1-2 (November 24, 2011): 263–73. http://dx.doi.org/10.1007/s11071-011-0262-5.

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38

Zou, Shaohui, and Tian Zhang. "Multifractal Detrended Cross-Correlation Analysis of Electricity and Carbon Markets in China." Mathematical Problems in Engineering 2019 (May 14, 2019): 1–13. http://dx.doi.org/10.1155/2019/9350940.

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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.
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SHANG, PENGJIAN, KEQIANG DONG, and SANTI KAMAE. "MODELING CROSS-CORRELATIONS OF TRAFFIC FLOW." International Journal of Bifurcation and Chaos 20, no. 10 (October 2010): 3323–28. http://dx.doi.org/10.1142/s0218127410027714.

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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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40

Zhang, Qiaoyan, Lixian Wang, Shang Jin, Xiaozhen Hao, and Zhenlong Chen. "Asymmetric Multifractal Analysis of Rebar Futures and Spot Market in China." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 3 (May 20, 2020): 282–92. http://dx.doi.org/10.20965/jaciii.2020.p0282.

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In this study, a wavelet denoising method is first used to eliminate the influence of noise. Then, an overlapping smooth window technique is introduced into the asymmetric multifractal detrended cross-correlation analysis method, which was combined with the multiscale multifractal analysis method, resulting in the proposed asymmetric multiscale multifractal detrended cross-correlation analysis method. This method not only remedies the pseudo-fluctuation defect of the traditional method, but also explores the asymmetric multifractal cross-correlation between China’s rebar futures and spot markets at different scales. The results show the existence of an asymmetric multifractal cross-correlation between rebar futures and spot markets with upward and downward trends at different scales. This cross-correlation is highly complex at the small-scale, and more pronounced when the futures market is in an uptrend.
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SONG, JIE, and PENGJIAN SHANG. "EFFECT OF LINEAR AND NONLINEAR FILTERS ON MULTIFRACTAL DETRENDED CROSS-CORRELATION ANALYSIS." Fractals 19, no. 04 (December 2011): 443–53. http://dx.doi.org/10.1142/s0218348x11005464.

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When probing the dynamical properties of complex systems, such as physical and physiological systems, the output signal may be not the expected one. It is often a linear or nonlinear filter (or a transformation) of the right one represented the properties we want to investigate. Besides, for a multiple-component system, it is necessary to consider the relations between different influence factors. Here, we investigate what effect kinds of linear and nonlinear filters have on the cross-correlation properties of monofractal series and binomial multifractal series relatively. We use the multifractal detrended cross-correlation analysis (MFDCCA) that has been known well for its accurate quantization of cross-correlations between two time series. We study the effect of five filters: (i) linear (yi = axi + b); (ii) polynomial [Formula: see text]; (iii) logarithmic (yi = log (xi + δ)); (iv) exponential (yi = exp (axi + b)); and (v) power-law (yi = (xi + a)b). We find that for both monofractal and multifractal signals, linear filters have no effect on the cross-correlation properties while the influence of polynomial, logarithmic and power-law filters mainly depends on (a) the strength of cross-correlations in the original series; (b) the parameter b of the polynomial filter; (c) the offset δ in the logarithmic filter; and (d) both the parameter a and b of the power-law filter. In addition, the parameter a and b of the exponential filter change the cross-correlation properties of monofractal signal, yet they have little influence on that of multifractal signal.
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42

Huang, Jingjing, and Danlei Gu. "Multiscale Multifractal Detrended Cross-Correlation Analysis of High-Frequency Financial Time Series." Fluctuation and Noise Letters 18, no. 03 (July 16, 2019): 1950014. http://dx.doi.org/10.1142/s0219477519500147.

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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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43

Lonardoni Paulino Schiavon, Luiza, and Antônio Fernando Crepaldi. "The use of a Detrended Cross-Correlation Analysis on returns from agricultural commodities in the subprime crisis." Revista Gestão da Produção Operações e Sistemas 16, no. 03 (September 23, 2021): 119–37. http://dx.doi.org/10.15675/gepros.v16i3.2795.

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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.
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44

Cao, Guangxi, Yan Han, Yuemeng Chen, and Chunxia Yang. "Multifractal detrended cross-correlation between the Chinese domestic and international gold markets based on DCCA and DMCA methods." Modern Physics Letters B 28, no. 11 (May 9, 2014): 1450090. http://dx.doi.org/10.1142/s0217984914500900.

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Based on the daily price data of Shanghai and London gold spot markets, we applied detrended cross-correlation analysis (DCCA) and detrended moving average cross-correlation analysis (DMCA) methods to quantify power-law cross-correlation between domestic and international gold markets. Results show that the cross-correlations between the Chinese domestic and international gold spot markets are multifractal. Furthermore, forward DMCA and backward DMCA seems to outperform DCCA and centered DMCA for short-range gold series, which confirms the comparison results of short-range artificial data in L. Y. He and S. P. Chen [Physica A 390 (2011) 3806–3814]. Finally, we analyzed the local multifractal characteristics of the cross-correlation between Chinese domestic and international gold markets. We show that multifractal characteristics of the cross-correlation between the Chinese domestic and international gold markets are time-varying and that multifractal characteristics were strengthened by the financial crisis in 2007–2008.
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45

YAO, CAN-ZHONG, JI-NAN LIN, and XU-ZHOU ZHENG. "MULTIFRACTAL DETRENDED CROSS-CORRELATION ANALYSIS FOR LARGE-SCALE WAREHOUSE-OUT BEHAVIORS." Fractals 23, no. 04 (December 2015): 1550044. http://dx.doi.org/10.1142/s0218348x15500449.

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Based on cross-correlation algorithm, we analyze the correlation property of warehouse-out quantity of different warehouses, respectively, and different products of each warehouse. Our study identifies that significant cross-correlation relationship for warehouse-out quantity exists among different warehouses and different products of a warehouse. Further, we take multifractal detrended cross-correlation analysis for warehouse-out quantity among different warehouses and different products of a warehouse. The results show that for the warehouse-out behaviors of total amount, different warehouses and different products of a warehouse significantly follow multifractal property. Specifically for each warehouse, the coupling relationships of rebar and wire rod reveal long-term memory characteristics, no matter for large fluctuation or small one. The cross-correlation effect on long-range memory property among warehouses probably has less to do with product types,and the long-term memory of YZ warehouse is greater than others especially in total amount and wire rod product. Finally, we shuffle and surrogate data to explore the source of multifractal cross-correlation property in logistics system. Taking the total amount of warehouse-out quantity as example, we confirm that the fat-tail distribution of warehouse-out quantity sequences is the main factor for multifractal cross-correlation. Through comparing the performance of the multifractal detrended cross-correlation analysis (MF-DCCA), centered multifractal detrending moving average cross-correlation analysis (MF-X-DMA) algorithms, the forward and backward MF-X-DMA algorithms, we find that the forward and backward MF-X-DMA algorithms exhibit a better performance than the other ones.
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46

Plocoste, Thomas, and Pablo Pavón-Domínguez. "Multifractal detrended cross-correlation analysis of wind speed and solar radiation." Chaos: An Interdisciplinary Journal of Nonlinear Science 30, no. 11 (November 2020): 113109. http://dx.doi.org/10.1063/5.0026354.

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47

Zhang, Ningning, Aijing Lin, and Pengbo Yang. "Detrended moving average partial cross-correlation analysis on financial time series." Physica A: Statistical Mechanics and its Applications 542 (March 2020): 122960. http://dx.doi.org/10.1016/j.physa.2019.122960.

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48

Zhuang, Xiaoyang, Yu Wei, and Bangzheng Zhang. "Multifractal detrended cross-correlation analysis of carbon and crude oil markets." Physica A: Statistical Mechanics and its Applications 399 (April 2014): 113–25. http://dx.doi.org/10.1016/j.physa.2013.12.048.

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49

Jiang, Shan, Bao-Gen Li, Zu-Guo Yu, Fang Wang, Vo Anh, and Yu Zhou. "Multifractal temporally weighted detrended cross-correlation analysis of multivariate time series." Chaos: An Interdisciplinary Journal of Nonlinear Science 30, no. 2 (February 2020): 023134. http://dx.doi.org/10.1063/1.5129574.

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50

Shadkhoo, S., and G. R. Jafari. "Multifractal detrended cross-correlation analysis of temporal and spatial seismic data." European Physical Journal B 72, no. 4 (December 2009): 679–83. http://dx.doi.org/10.1140/epjb/e2009-00402-2.

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