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

Abstract

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

Time-frequency analysis of radon and thoron data using continuous wavelet transform

“…The dataset included 15692 observations of radon and thoron as well as environmental factors such as pressure, relative humidity, and temperature. Further experimental details can be found elsewhere [24,[53][54][55][56][57][58].…”

The Evaluation of Mean-Detrended Cross-Correlation Analysis Portfolio Strategy for Multiple risk Assets

“…Given that the DCCA can accurately describe the correlation between security prices under multiple time scales, it is a powerful tool for depicting fractal correlation (Ferreira et al, 2019;Iqbal et al, 2020). Therefore, to precisely survey the fractal correlation characteristics among assets in the securities investment portfolio under the fractal market and construct an effective securities investment portfolio strategy to overcome the inability of traditional correlation surveys in the M-V portfolio strategy to accurately depict the disadvantages of the fractal correlation among assets, DCCA was considered in the research on the mean-variance securities investment portfolio strategy as follows.…”

“…To incorporate the fractal correlation between securities to optimize the securities investment portfolio strategy, the natural solution is to construct an index that can survey the fractal correlation and embed them into the meanvariance criterion of MPT to build the securities investment portfolio strategy instead of the survey that cannot review the fractal correlation. In the selection of methods, combined with the above mentioned (Chun et al, 2020;Zhou, 2008), DCCA can characterize the correlation among time series at multiple time scales, which is a useful tool for describing fractal correlation and has been widely used in many fields (Iqbal et al, 2020;Podobnik & Stanley, 2008). Compared with the VaR, CVaR, and risk surveys built on this basis, DCCA can better deal with the nonlinear problem of security price when the real security market is a fractal market, and the risk measurement index constructed according to it can better adapt to the nonlinear and non-normal complex financial environment, and the result will be more convincing and universal.…”

An Improved Imputation Method for Accurate Prediction of Imputed Dataset Based Radon Time Series

“…Radon and thoron time series are subject to nonlinear processes and extracting some meaningful information from such series is not an easy task and needs the use of modern computational techniques. Detrended fluctuation analysis (DFA), detrended cross-correlation analysis (DCCA), and multifractal detrended fluctuation analysis (MF-DFA) of soil radon ( 222 Rn) and thoron( 220 Rn) time series have been used to find long-range correlations and characterization of correlated data of more than one non-stationary time series and to examine the scaling and multifractal features of radon and thoron time series [34], [35].…”