Yuan, N.; Fu, Z.; Zhang, H.; Piao, L.; Xoplaki, E. & Luterbacher, J. (2015): <b>Detrended partial-cross-correlation analysis: A new method for analyzing correlations in complex system</b>. <i>Scientific Reports</i> <b>5</b>, 08143<br>DOI: <a href="http://dx.doi.org/10.1038/srep08143" target="_blank">http://dx.doi.org/10.1038/srep08143</a>.
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Title:
Detrended partial-cross-correlation analysis: A new method for analyzing correlations in complex system
In this paper, a new method, detrended partial-cross-correlation analysis (DPCCA), is proposed. Based on<br/>
detrended cross-correlation analysis (DCCA), this method is improved by including partial-correlation<br/>
technique, which can be applied to quantify the relations of two non-stationary signals (with influences of<br/>
other signals removed) on different time scales. We illustrate the advantages of this method by performing<br/>
two numerical tests. Test I shows the advantages of DPCCA in handling non-stationary signals, while Test II<br/>
reveals the ‘‘intrinsic’’ relations between two considered time series with potential influences of other<br/>
unconsidered signals removed. To further show the utility of DPCCA in natural complex systems, we<br/>
provide new evidence on the winter-time Pacific Decadal Oscillation (PDO) and the winter-time Nino3 Sea<br/>
Surface Temperature Anomaly (Nino3-SSTA) affecting the Summer Rainfall over the middle-lower reaches<br/>
of the Yangtze River (SRYR). By applying DPCCA, better significant correlations between SRYR and<br/>
Nino3-SSTA on time scales of 6 , 8 years are found over the period 1951 , 2012, while significant<br/>
correlations between SRYR and PDO on time scales of 35 years arise. With these physically explainable<br/>
results, we have confidence that DPCCA is an useful method in addressing complex systems.
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