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

G.F. Zebende, A.A. Brito and A.P. Castro

Physica A: Statistical Mechanics and its Applications, 2020, vol. 545, issue C

Abstract: 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.

Keywords: DFA; DCCA; Detrended cross-correlation coefficient; Long-range memory time-series; ARFIMA process (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:545:y:2020:i:c:s0378437119319399

DOI: 10.1016/j.physa.2019.123472

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