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

被引：11

作者：

Zebende, G. F. ^{[1
]}

Brito, A. A. ^{[2
,3
]}

Castro, A. P. ^{[4
]}

机构：

[1] State Univ Feira de Santana, Feira De Santana, BA, Brazil

[2] Fed Inst Educ Sci & Technol, Paulo Afonso, BA, Brazil

[3] Senai, Salvador, BA, Brazil

[4] Jorge Amado Univ Ctr, Salvador, BA, Brazil

来源：

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2020年 / 545卷

关键词：

DFA; DCCA; Detrended cross-correlation coefficient; Long-range memory time-series; ARFIMA process; CORRELATION-COEFFICIENT; AIR-TEMPERATURE; EXCHANGE; OIL;

D O I：

10.1016/j.physa.2019.123472

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

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.

引用

页数：7

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