Yuan, N.; Xoplaki, E.; Zhu, C. & Luterbacher, J. (2016): <b>A novel way to detect correlations on multi-time scales, with temporal evolution and for multi-variables</b>. <i>Scientific Reports</i> <b>6</b>, 27707<br>DOI: <a href="http://dx.doi.org/10.1038/srep27707" target="_blank">http://dx.doi.org/10.1038/srep27707</a>.
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Title:
A novel way to detect correlations on multi-time scales, with temporal evolution and for multi-variables
In this paper, two new methods, Temporal evolution of Detrended Cross-Correlation Analysis (TDCCA)<br/>
and Temporal evolution of Detrended Partial-Cross-Correlation Analysis (TDPCCA), are proposed<br/>
by generalizing DCCA and DPCCA. Applying TDCCA/TDPCCA, it is possible to study correlations on<br/>
multi-time scales and over different periods. To illustrate their properties, we used two climatological<br/>
examples: i) Global Sea Level (GSL) versus North Atlantic Oscillation (NAO); and ii) Summer Rainfall<br/>
over Yangtze River (SRYR) versus previous winter Pacific Decadal Oscillation (PDO). We find significant<br/>
correlations between GSL and NAO on time scales of 60 to 140 years, but the correlations are nonsignificant<br/>
between 1865–1875. As for SRYR and PDO, significant correlations are found on time<br/>
scales of 30 to 35 years, but the correlations are more pronounced during the recent 30 years. By<br/>
combining TDCCA/TDPCCA and DCCA/DPCCA, we proposed a new correlation-detection system,<br/>
which compared to traditional methods, can objectively show how two time series are related (on<br/>
which time scale, during which time period). These are important not only for diagnosis of complex<br/>
system, but also for better designs of prediction models. Therefore, the new methods offer new<br/>
opportunities for applications in natural sciences, such as ecology, economy, sociology and other<br/>
research fields.
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