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