We provide a non-parametric computing-intensive method to test the statistical significance of the rolling window correlation for bi-variate time series. This method (test) addresses the effects due to the multiple testing (inflation of the Type I error) when the statistical significance is estimated for the rolling window correlation coefficients. We follow Telford and Polanco-Martínez to carry out the proposed method. The method is based on Monte Carlo simulations by permuting one of the variables (dependent) under analysis and keeping fixed the other variable (independent). We improve the computational time of this method to reduce the computation time (speedup was up to practically five times faster than the sequential method using 11 cores) through parallel computing. We compare the results obtained through the proposed method with two p-value correction methods frequently used (Bonferroni and Benjamini and Hochberg –BH) after being applied to synthetic and to real-life ecological time series. Our results show that the proposed method works roughly similar to these two p-value correction methods, especially with the method of BH, but our test is a little more restrictive than BH and a little more permissive than Bonferroni. The test is programmed in R and is included in the package NonParRolCor that is available freely on CRAN.

我们提供了一种非参数计算密集型方法来测试双变量时间序列的滚动窗口相关性的统计显着性。当估计滚动窗口相关系数的统计显着性时，该方法（测试）解决了由于多重测试（I 类错误的膨胀）而产生的影响。我们遵循 Telford 和 Polanco-Martínez 来执行所提出的方法。该方法基于蒙特卡罗模拟，通过排列分析中的一个变量（相关变量）并保持另一个变量（独立变量）固定不变。我们改进了这种方法的计算时间，以通过并行计算来减少计算时间（加速比使用 11 个内核的顺序方法快五倍）。我们将通过所提出的方法获得的结果与两个在应用于合成和现实生态时间序列后，经常使用p值校正方法（Bonferroni 和 Benjamini 和 Hochberg –BH）。我们的结果表明，所提出的方法与这两种p值校正方法大致相似，尤其是 BH 方法，但我们的测试比 BH 更严格，比 Bonferroni 更宽松。该测试是用 R 编程的，包含在CRAN 上免费提供的NonParRolCor包中。