DCCA cross-correlation analysis in time-series with removed parts

Zebende, Gilney Figueira; Brito, Adriana; Castro, Arleys Pereira Nunes de

doi:10.1016/j.physa.2019.123472

Cited by 13 publications

(13 citation statements)

References 28 publications

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“…Then, we generalized the DFA algorithm (six steps described above in DFA algorithm) to find

11

(covariance of the residuals) and the detrended function by:

But, for quantify the level of cross-correlation, the DCCA cross-correlation coefficient can defined as the ratio between the detrended cross-correlation function,

, and the detrended auto-correlation function,

and

, for the time-series

and

, respectively:

Some properties of

naturally appear, the most important is that:

1.0 1.0

In this case,

0.0

means there is no cross-correlation between

and

, and it splits the level of cross-correlation between positive and the negative case. This coefficient has been tested on selected time-series [21] , [42] and proved to be quite robust, mainly for statistical analysis between non-stationary time-series [43] , [44] , [45] , [46] , [47] , [48] , among other cases. It is noteworthy that there is the

generalization, for more than two time-series analysis, what we call multiple DCCA coefficient,

…”

Section: Literature Revision and Methodologymentioning

confidence: 99%

Stock market efficiency: An intraday case of study about the G-20 group

Zebende

Dias

Aguiar

2022

Heliyon

Self Cite

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“…Then, we generalized the DFA algorithm (six steps described above in DFA algorithm) to find

11

(covariance of the residuals) and the detrended function by:

But, for quantify the level of cross-correlation, the DCCA cross-correlation coefficient can defined as the ratio between the detrended cross-correlation function,

, and the detrended auto-correlation function,

and

, for the time-series

and

, respectively:

Some properties of

naturally appear, the most important is that:

1.0 1.0

In this case,

0.0

means there is no cross-correlation between

and

generalization, for more than two time-series analysis, what we call multiple DCCA coefficient,

…”

Section: Literature Revision and Methodologymentioning

confidence: 99%

Stock market efficiency: An intraday case of study about the G-20 group

Zebende

Dias

Aguiar

2022

Heliyon

Self Cite

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“…In brief, as methodology used by Peng et al 1993 and Zebende et al 2019 [17,38], each of the time series (of total length = N 13116) is integrated first as shown here for radon, Thoron, temperature and pressure time series;…”

Section: Detrended Fluctuation Analysis (Dfa) Methodologymentioning

confidence: 99%

“…Zebende et al 2019 analyzed the relation between original time-series and removing pieces in time-series with long range-memory by DFA method and detrended cross-correlation coefficient. Results of both time-series shows no changes for DFA and DCCA cross-correlations [38]. Zeng et al 2016, employed the DCCA cross-correlation coefficient ( r DCCA ( ) to quantify the cross-correlations between wind speed components, and instantaneous wind speed components in indoor and outdoor environments [39].…”

Section: Introductionmentioning

confidence: 99%

Detrended cross correlation analysis (DCCA) of radon, thoron, temperature and pressure time series data

et al. 2020

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Non-linear processes evolving within earth crust and in atmosphere gives complex time series and extracting meaningful physical information from such data is not easy without development and practice of modern computational techniques. Detrended fluctuation analysis (DFA), and detrended cross correlation analysis (DCCA), are used to explore long-range correlations and characterization of correlated data of more than one non-stationary time series. We present results of DFA, and DCCA techniques applied on radon, thoron, temperature and pressure time series. Time series data of each series have been decomposed for each season and seasonal periodicities have been removed. DFA and DCCA techniques have been employed on the deseasonalized data. For all four seasons, DFA scaling exponent ( ) and correlation exponent ( ) have been calculated for radon, thoron, temperature and pressure time series. The results of ‘ ’ and ‘r’, for each of the time series have been used for finding the existence of persistency in processes and investigation of long-range correlations. Largely, these results indicate that the cross-correlation relationship between observed times series for each season is not of simply power-law type. Detrended Cross Correlation Analysis between different time series shows persistent behavior. In order to quantify the level of cross-correlations, we computed the DCCA cross-correlation coefficient

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“…It is important to notice that ρ (X i ,X j ) depends on n (time scale). So, one of the advantages of this detrended cross-correlation coefficient is to measure cross-correlations between two non-stationary time series at different time scales [23][24][25][26][27][28] .…”

Section: The Dcca Cross-correlation Coefficientmentioning

confidence: 99%

Networks Analysis Of Brazilian Climate Data Based On The DCCA Cross-Correlation Coefficient

Rodrigues

Filho

Guedes

2022

Preprint

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Climate change is one of the most relevant challenges that the world has to deal with. A better understanding about the behaviour of environmental and atmospheric variables, their long range cross correlation and the way they relate to each other, can provide helpful insights about how the climate is changing. However, such studies are complex and rarely found in the literature, especially dealing with data from the Brazilian territory. In this paper we consider four environmental and atmospheric variables, wind speed, radiation, temperature and humidity, measured in 27 stations (the capital of each of the 26 Brazilian states plus the federal district). We use the detrended fluctuation analysis to evaluate the statistical self-affinity of the signal, the cross correlation coefficient $\rho_{DCCA}$ to quantify the long range cross correlation between stations, and a network analysis that considers the top $10\%$ $\rho_{DCCA}$ values for each atmospheric variable to better understand the cross correlation between stations. The methodology used and combined in this paper represents a step forward in the field of time series and network analysis, that can be applied to other regions, other environmental variables and also to other fields of research. The results of the application are of great importance to better understand the behaviour of environmental and atmospheric variables in the Brazilian territory and to provide helpful insights about climate change and renewable energy production.

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DCCA cross-correlation analysis in time-series with removed parts

Cited by 13 publications

References 28 publications

Stock market efficiency: An intraday case of study about the G-20 group

Stock market efficiency: An intraday case of study about the G-20 group

Detrended cross correlation analysis (DCCA) of radon, thoron, temperature and pressure time series data

Networks Analysis Of Brazilian Climate Data Based On The DCCA Cross-Correlation Coefficient

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