A rigorous and versatile statistical test for correlations between stationary time series.

ABSTRACT: In disciplines from biology to climate science, a routine task is to compute a correlation between a pair of time series and determine whether the correlation is statistically significant (i.e., unlikely under the null hypothesis that the time series are independent). This problem is challenging because time series typically exhibit autocorrelation and thus cannot be properly analyzed with the standard iid-oriented statistical tests. Although there are well-known parametric tests for time series, these are designed for linear correlation statistics and thus not suitable for the increasingly popular nonlinear correlation statistics. There are also nonparametric tests that can be used with any correlation statistic, but for these, the conditions that guarantee correct false positive rates ar

SUBMITTER: Yuan AE 

PROVIDER: S-EPMC11398661 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature