Local detrended cross-correlation analysis for non-stationary time series

We propose a method called local detrended cross-correlation analysis (LDCCA) to quantify temporal power-law cross-correlation characteristics of coupled time series at local samples. The proposed method is validated with uncoupled Gaussian white noises, coupled ARFIMA processes and Hénon maps. As an example, electrical probe technologies are employed to detect the flow structure information of gas-liquid churn flows in a vertical pipe, and temporal cross-correlation characteristics of flow interfacial structures are investigated using the proposed LDCCA. The results show that the proposed LDCCA can provide beneficial insights to local dynamic evolution behaviors of the flow interfacial structures.