EMD Method for Minimizing the Effect of Seasonal Trends in Detrended Cross-Correlation Analysis

Mathematical Problems in Engineering - Tập 2013 - Trang 1-7 - 2013
Kuiran Dong1, You Gao1, Nianpeng Wang2
1College of Science, Civil Aviation University of China, Tianjin, 300300, China
2College of Science, Hebei University of Engineering, Handan 056038, China

Tóm tắt

Detrended cross-correlation analysis (DCCA) is a scaling method commonly used to estimate long-range power-law cross-correlation in nonstationary signals. Recent studies have reported signals superimposed with trends, which often lead to the complexity of the signals and the susceptibility of DCCA. This paper artificially generates long-range cross-correlated signals and systematically investigates the effect of seasonal trends. Specifically, for the crossovers raised by trends, we propose a smoothing algorithm based on empirical mode decomposition (EMD) method which decomposes underlying signals into several intrinsic mode functions (IMFs) and a residual trend. After the removal of slowly oscillating components and residual term, seasonal trends are eliminated.

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Mathematical Problems in Engineering

Tập 2013

1-7

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