Detrended cross-correlation analysis for non-stationary time series with periodic trends

Abstract

Noisy signals in many real-world systems display long-range autocorrelations and long-range cross-correlations. Due to periodic trends, these correlations are difficult to quantify. We demonstrate that one can accurately quantify power-law cross-correlations between different simultaneously recorded time series in the presence of highly non-stationary sinusoidal and polynomial overlying trends by using the new technique of detrended cross-correlation analysis with varying order ℓ of the polynomial. To demonstrate the utility of this new method —which we call DCCA-ℓ(n), where n denotes the scale— we apply it to meteorological data.