We describe two families of statistical tests to detect partial correlation in vectorial timeseries. The tests measure whether an observed timeseries Y can be predicted from a second series X, even after accounting for a third series Z which may correlate with X. They do not make any assumptions on the nature of these timeseries, such as stationarity or linearity, but they do require that multiple statistically independent recordings of the 3 series are available. Intuitively, the tests work by asking if the series Y recorded on one experiment can be better predicted from X recorded on the same experiment than on a different experiment, after accounting for the prediction from Z recorded on both experiments.

翻译：我们描述了两种统计检验族，用于检测向量时间序列中的偏相关性。这些检验衡量的是：即使在考虑可能与X相关的第三个序列Z之后，观测时间序列Y是否仍可由第二个序列X预测。这些检验不对时间序列的性质（如平稳性或线性）做任何假设，但要求三个序列具有多次统计独立的记录。直观上，该检验通过以下方式实现：在同时使用两个实验记录的Z进行预测后，比较同一实验记录的X对Y的预测效果是否显著优于不同实验记录的X的预测效果。