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

In this paper we analyze the effect of removing pieces in time-series with long range-memory by DFA method and detrended cross-correlation coefficient. To achieve this purpose, initially simulated time-series are produced by ARFIMA process (long time dependence). From these simulated time-series cuts and removals are produced. The results show that for up to 50% of removed parts, compared to the original time-series, there is no change in the final results for detrended auto and cross-correlations. Therefore, with this paper we show that the DFA method and the detrended cross-correlation coefficient are robust for time-series analysis even for time-series with removed parts. This result ensures that these methods can be applied to real time-series, which in many cases lacks measurement for a variety of reasons and causes. (C) 2019 Elsevier B.V. All rights reserved.